Machine learning with a reject option: A survey

Untitled work

(1996) · Cited by 20,179 · see citing works

Advances in neural information processing systems 7

(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,759 · see citing works

Advances in Neural Information Processing Systems 25

Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653 · see citing works

Isolation Forest

Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008) · 2008 Eighth IEEE International Conference on Data Mining · doi:10.1109/icdm.2008.17 · Cited by 6,601 · see citing works

Untitled work

(2009) · Minds at UW (University of Wisconsin) · Cited by 4,791 · see citing works

Methods of combining multiple classifiers and their applications to handwriting recognition

Xu, L., Krzyżak, A., & Suen, C. Y. (1992) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.155943 · Cited by 2,192 · see citing works

Management of validation of HPLC method for determination of acetylsalicylic acid impurities in a new pharmaceutical product

Kowalska, M. K., Woźniak, M., Kijek, M. M., Mitrosz, P., Szakiel, J., & Turek, P. (2022) · Scientific Reports · doi:10.1038/s41598-021-99269-x · Cited by 2,121 · see citing works

Support Vector Method for Novelty Detection

Schölkopf, B., Williamson, R. C., Smola, A. J., Shawe-Taylor, J. S., & Platt, J. C. (1999) · Cited by 2,038 · see citing works

Efficient algorithms for mining outliers from large data sets

Ramaswamy, S., Rastogi, R. R., & Shim, K. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335437 · Cited by 1,908 · see citing works

A review of novelty detection

Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014) · Signal Processing · doi:10.1016/j.sigpro.2013.12.026 · Cited by 1,649 · see citing works

Novelty detection: a review—part 1: statistical approaches

Markou, M., & Singh, S. (2003) · Signal Processing · doi:10.1016/j.sigpro.2003.07.018 · Cited by 1,435 · see citing works

IEEE Transactions on Pattern Analysis and Machine Intelligence

(1978) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.1978.308564 · Cited by 1,328 · see citing works

On optimum recognition error and reject tradeoff

Chow, C.-K. (1970) · IEEE Transactions on Information Theory · doi:10.1109/tit.1970.1054406 · Cited by 1,303 · see citing works

Weighted extreme learning machine for imbalance learning

Zong, W., Huang, G.-B., & Chen, Y. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.08.010 · Cited by 750 · see citing works

An Introduction to Conditional Random Fields

Sutton, C. A., & McCallum, A. (2012) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000013 · Cited by 624 · see citing works

A method of combining multiple experts for the recognition of unconstrained handwritten numerals

Huang, Y., & Suen, C. Y. (1995) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.368145 · Cited by 566 · see citing works

Second opinion needed: Communicating uncertainty in medical machine learning

Kompa, B., Snoek, J., & Beam, A. L. (2021) · npj Digital Medicine · doi:10.1038/s41746-020-00367-3 · Cited by 514 · see citing works

Generalized ODIN: Detecting Out-of-Distribution Image Without Learning From Out-of-Distribution Data

Hsu, Y.-C., Shen, Y., Jin, H., & Kira, Z. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01096 · Cited by 499 · see citing works

A comprehensive survey and analysis of generative models in machine learning

Harshvardhan, G. M., Gourisaria, M. K., Pandey, M., & Rautaray, S. S. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100285 · Cited by 437 · see citing works

A survey on instance selection for active learning

Fu, Y., Zhu, X., & Ли, Б. (2012) · Knowledge and Information Systems · doi:10.1007/s10115-012-0507-8 · Cited by 368 · see citing works

Classification with a Reject Option using a Hinge Loss

Bartlett, P. L., & Wegkamp, M. H. (2008) · QUT ePrints (Queensland University of Technology) · doi:10.5555/1390681.1442792 · Cited by 326 · see citing works

To Trust Or Not To Trust A ClassifierPreprint

Jiang, H., Kim, B., Guan, M. Y., & Gupta, M. R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.11783 · Cited by 320 · see citing works

Democracy in neural nets: Voting schemes for classification

Battiti, R., & Colla, A. M. (1994) · Neural Networks · doi:10.1016/0893-6080(94)90046-9 · Cited by 308 · see citing works

Optimal combinations of pattern classifiers

Lam, L., & Suen, C. Y. (1995) · Pattern Recognition Letters · doi:10.1016/0167-8655(95)00050-q · Cited by 307 · see citing works

MetaFraud: A Meta-Learning Framework for Detecting Financial Fraud1

Abbasi, A., Albrecht, C., Vance, A., & Hansen, J. (2012) · MIS Quarterly · doi:10.2307/41703508 · Cited by 298 · see citing works

Metalearning

Brazdil, P., Giraud-Carrier, C. G., Soares, C., & Vilalta, R. (2008) · Cognitive technologies · doi:10.1007/978-3-540-73263-1 · Cited by 268 · see citing works

Selective Classification for Deep Neural Networks

Geifman, Y., & El-Yaniv, R. (2017) · arXiv · doi:10.48550/arxiv.1705.08500 · Cited by 220 · see citing works

The Nearest Neighbor Classification Rule with a Reject Option

Hellman, M. E. (1970) · IEEE Transactions on Systems Science and Cybernetics · doi:10.1109/tssc.1970.300339 · Cited by 219 · see citing works

To reject or not to reject: that is the question-an answer in case of neural classifiers

Stefano, C. D., Sansone, C., & Vento, M. (2000) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/5326.827457 · Cited by 218 · see citing works

Classification with reject option

Herbei, R., & Wegkamp, M. H. (2006) · Canadian Journal of Statistics · doi:10.1002/cjs.5550340410 · Cited by 203 · see citing works

Handwritten zip code recognition with multilayer networks

LeCun, Y., Matan, O., Boser, B. E., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., Jacket, L. D., & Baird, H. S. (2002) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.1990.119325 · Cited by 198 · see citing works

On the error-reject trade-off in biometric verification systems

Golfarelli, M., Maio, D., & Malton, D. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598237 · Cited by 193 · see citing works

A statistical decision rule with incomplete knowledge about classes

Dubuisson, B., & Masson, M. (1993) · Pattern Recognition · doi:10.1016/0031-3203(93)90097-g · Cited by 183 · see citing works

On the Foundations of Noise-free Selective Classification

El‐Yaniv, R., & Wiener, Y. (2010) · Journal of Machine Learning Research · Cited by 170 · see citing works

Learning to Classify Ordinal Data: The Data Replication Method

Cardoso, J. S., & da Costa, J. P. (2007) · doi:10.5555/1314498.1314546 · Cited by 163 · see citing works

Learning with Rejection

Cortes, C., DeSalvo, G., & Mohri, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46379-7_5 · Cited by 162 · see citing works

SelectiveNet: A Deep Neural Network with an Integrated Reject OptionPreprint

Geifman, Y., & El‐Yaniv, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.09192 · Cited by 157 · see citing works

Moderating the outputs of support vector machine classifiers

Kwok, J. T.-Y. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.788642 · Cited by 155 · see citing works

Support Vector Machines with Embedded Reject Option

Fumera, G., & Roli, F. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45665-1_6 · Cited by 153 · see citing works

Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty

Senge, R., Bösner, S., Dembczyński, K., Haasenritter, J., Hirsch, O., Donner‐Banzhoff, N., & Hüllermeier, E. (2013) · Information Sciences · doi:10.1016/j.ins.2013.07.030 · Cited by 146 · see citing works

Gaussian Process Regression: Active Data Selection and Test Point Rejection

Seo, S., Wallat, M., Graepel, T., & Obermayer, K. (2000) · Informatik aktuell · doi:10.1007/978-3-642-59802-9_4 · Cited by 145 · see citing works

Concrete Dropout

Gal, Y., Hron, J., & Kendall, A. (2017) · neural information processing systems · Cited by 137 · see citing works

Reliable Classification of Vehicle Types Based on Cascade Classifier Ensembles

Zhang, B. (2012) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2012.2213814 · Cited by 135 · see citing works

Classification with confidence

Lei, J. (2014) · Biometrika · doi:10.1093/biomet/asu038 · Cited by 131 · see citing works

Self-Adaptive Training: beyond Empirical Risk MinimizationPreprint

Huang, L., Zhang, C., & Change Institutions to: University of Waterloo (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.10319 · Cited by 128 · see citing works

An approach to novelty detection applied to the classification of image regions

Singh, S., & Markou, M. (2004) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2004.1269665 · Cited by 117 · see citing works

Growing a multi-class classifier with a reject option

Tax, D. M. J., & Duin, R. P. W. (2008) · Pattern Recognition Letters · doi:10.1016/j.patrec.2008.03.010 · Cited by 117 · see citing works

Combating Label Noise in Deep Learning Using AbstentionPreprint

Thulasidasan, S., Tanmoy Bhattacharya, A., Bilmes, J., Chennupati, G., & Mohd-Yusof, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.10964 · Cited by 111 · see citing works

Support Vector Machines with a Reject Option

Grandvalet, Y., Rakotomamonjy, A., Keshet, J., & Canu, S. (2008) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 109 · see citing works

Evidence-based uncertainty sampling for active learning

Sharma, M., & Bilgic, M. (2016) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-016-0460-3 · Cited by 108 · see citing works

Online ensemble learning with abstaining classifiers for drifting and noisy data streams

Krawczyk, B., & Cano, A. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.12.008 · Cited by 107 · see citing works

A method for improving classification reliability of multilayer perceptrons

Cordella, L. P., Stefano, C. D., Tortorella, F., & Vento, M. (1995) · IEEE Transactions on Neural Networks · doi:10.1109/72.410358 · Cited by 100 · see citing works

Gaussian process regression: active data selection and test point rejection

Seo, S., Wallat, M., Graepel, T., & Obermayer, K. (2000) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2000.861310 · Cited by 96 · see citing works

Classification Methods with Reject Option Based on Convex Risk Minimization

Yuan, M., & Wegkamp, M. H. (2010) · Cited by 96 · see citing works

The optimum class-selective rejection rule

Ha, T. M. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.601248 · Cited by 94 · see citing works

Rotation-blended CNNs on a New Open Dataset for Tropical Cyclone Image-to-intensity Regression

Chen, B., Chen, B., & Lin, H.-T. (2018) · ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) · doi:10.1145/3219819.3219926 · Cited by 92 · see citing works

Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer

Madras, D., Pitassi, T., & Zemel, R. (2017) · arXiv · doi:10.48550/arxiv.1711.06664 · Cited by 90 · see citing works

The interaction between classification and reject performance for distance-based reject-option classifiers

Landgrebe, T. C. W., Tax, D. M. J., Paclı́k, P., & Duin, R. P. W. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.015 · Cited by 83 · see citing works

Blocking Transferability of Adversarial Examples in Black-Box Learning SystemsPreprint

Hosseini, H., Chen, Y., Kannan, S., Zhang, B., & Poovendran, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.04318 · Cited by 82 · see citing works

Semi-Supervised Anomaly Detection with an Application to Water Analytics

Vercruyssen, V., Meert, W., Verbruggen, G., Maes, K., Bäumer, R., & Davis, J. J. (2018) · Proceedings · doi:10.1109/icdm.2018.00068 · Cited by 78 · see citing works

Delegating classifiers

Ferri, C., Flach, P. A., & Hernández‐Orallo, J. (2004) · International Conference on Machine Learning · doi:10.1145/1015330.1015395 · Cited by 78 · see citing works

Classification with reject option in gene expression data

Hanczar, B., & Dougherty, E. R. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn349 · Cited by 76 · see citing works

Diagnostic of Pathology on the Vertebral Column with Embedded Reject Option

Neto, A. R. R., Sousa, R., Barreto, G. A., & Cardoso, J. S. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21257-4_73 · Cited by 76 · see citing works

Accuracy-Rejection Curves (ARCs) for Comparing Classification Methods with a Reject Option

Nadeem, M. S. A., Zucker, J., & Hanczar, B. (2009) · Cited by 76 · see citing works

Multi-label classification with a reject option

Pillai, I., Fumera, G., & Roli, F. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.01.035 · Cited by 75 · see citing works

Sleep Versus Wake Classification From Heart Rate Variability Using Computational Intelligence: Consideration of Rejection in Classification Models

Lewicke, A., Sazonov, E. E., Corwin, M. J., Neuman, M. R., & Schuckers, S. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2007.900558 · Cited by 74 · see citing works

Multistage pattern recognition with reject option

Pudil, P., Novovičová, J., Bláha, S., & Kittler, J. (2003) · IAPR International Conference on Pattern Recognition (ICPR) · doi:10.1109/icpr.1992.201729 · Cited by 73 · see citing works

Highly Accurate Recognition of Human Postures and Activities Through Classification With Rejection

Tang, W., & Sazonov, E. E. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2013.2287400 · Cited by 71 · see citing works

Deep neural rejection against adversarial examples

Sotgiu, A., Demontis, A., Melis, M., Biggio, B., Fumera, G., Feng, X., & Roli, F. (2020) · EURASIP Journal on Information Security · doi:10.1186/s13635-020-00105-y · Cited by 62 · see citing works

Confidence-based classifier design

Li, M., & Sethi, I. K. (2006) · Pattern Recognition · doi:10.1016/j.patcog.2006.01.010 · Cited by 61 · see citing works

Consistent Estimators for Learning to Defer to an Expert

Mozannar, H., & Sontag, D. (2020) · arXiv · doi:10.48550/arxiv.2006.01862 · Cited by 61 · see citing works

Untitled work

(2019) · Cited by 60 · see citing works

Support vector machines with a reject optionPreprint

Wegkamp, M. H., & Yuan, M. (2011) · Cited by 58 · see citing works

Confidence Estimation via Auxiliary Models

Corbière, C., Thome, N., Saporta, A., Vu, T.-H., Cord, M., & Pérez, P. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3085983 · Cited by 57 · see citing works

An Optimal Reject Rule for Binary Classifiers

Tortorella, F. (2000) · Lecture notes in computer science · doi:10.1007/3-540-44522-6_63 · Cited by 56 · see citing works

On the use of ROC analysis for the optimization of abstaining classifiers

Pietraszek, T. (2007) · Machine Learning · doi:10.1007/s10994-007-5013-y · Cited by 56 · see citing works

Beyond Disagreement-based Agnostic Active LearningPreprint

Zhang, C., & Chaudhuri, K. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1407.2657 · Cited by 53 · see citing works

Optimizing abstaining classifiers using ROC analysis

Pietraszek, T. (2005) · International Conference on Machine Learning · doi:10.1145/1102351.1102435 · Cited by 52 · see citing works

Classifiers with a reject option for early time-series classification

Hatami, N., & Chira, C. (2013) · doi:10.1109/ciel.2013.6613134 · Cited by 51 · see citing works

A support vector machines-based rejection technique for speech recognition

Ma, C., Randolph, M. A., & Drish, J. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2001.940847 · Cited by 51 · see citing works

Deep Gamblers: Learning to Abstain with Portfolio TheoryPreprint

Ziyin, L., Wang, Z., Liang, P. P., Salakhutdinov, R. R., Morency, L., & Ueda, M. (2019) · arXiv (Cornell University) · Cited by 49 · see citing works

AN EVALUATION OF MULTI-EXPERT CONFIGURATIONS FOR THE RECOGNITION OF HANDWRITTEN NUMERALS

Rahman, F., & Fairhurst, M. C. (1998) · Pattern Recognition · doi:10.1016/s0031-3203(97)00161-1 · Cited by 49 · see citing works

Analysis of error-reject trade-off in linearly combined multiple classifiers

Fumera, G., & Roli, F. (2004) · Pattern Recognition · doi:10.1016/j.patcog.2003.12.005 · Cited by 46 · see citing works

Classification with reject option for software defect prediction

Mesquita, D. P. P., Rocha, L. S., Gomes, J. P. P., & Neto, A. R. R. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.06.023 · Cited by 45 · see citing works

On optimal reject rules and ROC curves

Pereira, C. S., & Pires, A. M. (2004) · Pattern Recognition Letters · doi:10.1016/j.patrec.2004.09.042 · Cited by 43 · see citing works

Investigating feedforward neural networks with respect to the rejection of spurious patterns

Vasconcelos, G. C., Fairhurst, M. C., & Bisset, D. L. (1995) · Pattern Recognition Letters · doi:10.1016/0167-8655(94)00092-h · Cited by 42 · see citing works

Datasets meta-feature description for recommending feature selection algorithm

Filchenkov, A. A., & Pendryak, A. (2015) · Artificial Intelligence and Natural Language · doi:10.1109/ainl-ismw-fruct.2015.7382962 · Cited by 42 · see citing works

Deep Gamblers: Learning to Abstain with Portfolio TheoryPreprint

Ziyin, L., Wang, Z., Liang, P. P., Salakhutdinov, R., Morency, L., & Ueda, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.00208 · Cited by 40 · see citing works

Boosting with Abstention

Cortes, C., DeSalvo, G., & Mohri, M. (2016) · neural information processing systems · Cited by 39 · see citing works

A ROC-based reject rule for dichotomizers

Tortorella, F. (2004) · Pattern Recognition Letters · doi:10.1016/j.patrec.2004.09.004 · Cited by 39 · see citing works

Classifier design with incomplete knowledge

Muzzolini, R., Yang, Y., & Pierson, R. A. (1998) · Pattern Recognition · doi:10.1016/s0031-3203(97)00056-3 · Cited by 38 · see citing works

Design of reject rules for ECOC classification systems

Simeone, P., Marrocco, C., & Tortorella, F. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.08.001 · Cited by 37 · see citing works

Performance measures for classification systems with rejection

Condessa, F., Bioucas‐Dias, J. M., & Kovačević, J. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.10.011 · Cited by 37 · see citing works

An Ensemble of Rejecting Classifiers for Anomaly Detection of Audio Events

Conte, D., Foggia, P., Percannella, G., Saggese, A., & Vento, M. (2012) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2012.9 · Cited by 37 · see citing works

Optimal local rejection for classifiers

Fischer, L., Hammer, B., & Wersing, H. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.06.038 · Cited by 37 · see citing works

Active Learning with Abstaining Classifiers for Imbalanced Drifting Data Streams

Korycki, Ł., Cano, A., & Krawczyk, B. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006453 · Cited by 36 · see citing works

Multiclassification: reject criteria for the Bayesian combiner

Foggia, P., Sansone, C., Tortorella, F., & Vento, M. (1999) · Pattern Recognition · doi:10.1016/s0031-3203(98)00169-1 · Cited by 35 · see citing works

Agnostic Selective Classification

Wiener, Y., & El‐Yaniv, R. (2011) · neural information processing systems · Cited by 34 · see citing works

Reject option for VQ-based Bayesian classification

Vailaya, A., & Jain, A. (2002) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2000.906016 · Cited by 34 · see citing works

Rejection strategies and confidence measures for a k-NN classifier in an OCR task

Arlandis, J., Pérez-Cortés, J.-C., & Cano, J. (2003) · doi:10.1109/icpr.2002.1044806 · Cited by 33 · see citing works

Efficient rejection strategies for prototype-based classification

Fischer, L., Hammer, B., & Wersing, H. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.10.092 · Cited by 33 · see citing works

Classification with reject option in text categorisation systems

Fumera, G., Pillai, I., & Roli, F. (2004) · International Conference on Image Analysis and Processing · doi:10.1109/iciap.2003.1234113 · Cited by 33 · see citing works

Machine Learning with Abstention for Automated Liver Disease Diagnosis

Hamid, K., Asif, A., Abbasi, W. A., Sabih, D., & Minhas, F. (2017) · International Conference on Frontiers of Information Technology (FIT) · doi:10.1109/fit.2017.00070 · Cited by 33 · see citing works

Consistent algorithms for multiclass classification with an abstain option

Ramaswamy, H. G., Tewari, A., & Agarwal, S. (2018) · Electronic Journal of Statistics · doi:10.1214/17-ejs1388 · Cited by 32 · see citing works

Multiple Reject Thresholds for Improving Classification Reliability

Fumera, G., Roli, F., & Giacinto, G. (2000) · Lecture notes in computer science · doi:10.1007/3-540-44522-6_89 · Cited by 32 · see citing works

Application of optimum error-reject functions (Corresp.)

Fukunaga, K., & Kessell, D. (1972) · IEEE Transactions on Information Theory · doi:10.1109/tit.1972.1054919 · Cited by 32 · see citing works

Epistemic Uncertainty Sampling

Nguyen, V.-L., Destercke, S., & Hüllermeier, E. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-33778-0_7 · Cited by 31 · see citing works

Reducing the classification cost of support vector classifiers through an ROC-based reject rule

Tortorella, F. (2004) · Pattern Analysis and Applications · doi:10.1007/s10044-004-0209-2 · Cited by 31 · see citing works

Uncertainty-Aware Attention for Reliable Interpretation and PredictionPreprint

Heo, J., Lee, H., Kim, S., Lee, J., Kim, K. J., Yang, E., & Hwang, S. J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.09653 · Cited by 31 · see citing works

Uncertainty-Based Rejection in Machine Learning: Implications for Model Development and Interpretability

Barandas, M., Folgado, D., Santos, R., Simão, R., & Gamboa, H. F. S. (2022) · Electronics · doi:10.3390/electronics11030396 · Cited by 29 · see citing works

Siamese neural network based similarity metric for inertial gesture classification and rejection

Berlemont, S., Lefebvre, G., Duffner, S., & García, C. (2015) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2015.7163112 · Cited by 28 · see citing works

Improvement of reliability in banknote classification using reject option and local PCA

Ahmadi, A., Omatu, S., Fujinaka, T., & Kosaka, T. (2004) · Information Sciences · doi:10.1016/j.ins.2004.02.018 · Cited by 27 · see citing works

Lasso type classifiers with a reject option

Wegkamp, M. H. (2007) · Electronic Journal of Statistics · doi:10.1214/07-ejs058 · Cited by 26 · see citing works

CLASS-SELECTIVE REJECTION RULE TO MINIMIZE THE MAXIMUM DISTANCE BETWEEN SELECTED CLASSES

Horiuchi, T. (1998) · Pattern Recognition · doi:10.1016/s0031-3203(97)00136-2 · Cited by 26 · see citing works

Linear Classifier with Reject Option for the Detection of Vocal Fold Paralysis and Vocal Fold Edema

Kotropoulos, C. L., & Arce, G. R. (2009) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2009/203790 · Cited by 26 · see citing works

Online Learning with AbstentionPreprint

Cortes, C., DeSalvo, G., Gentile, C., Mohri, M., & Yang, S. C. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.03478 · Cited by 24 · see citing works

Uncertainty-Based Rejection Wrappers for Black-Box Classifiers

Mena, J., Pujol, O., & Vitria, J. M. (2020) · IEEE Access · doi:10.1109/access.2020.2996495 · Cited by 24 · see citing works

RO-SVM: Support Vector Machine with Reject Option for Image Categorization

Zhang, R., & Metaxas, D. (2006) · British Machine Vision Conference (BMVC) · doi:10.5244/c.20.123 · Cited by 23 · see citing works

Robust classification with reject option using the self-organizing map

Sousa, R., Neto, A. R. R., Cardoso, J. S., & Barreto, G. A. (2015) · Neural Computing and Applications · doi:10.1007/s00521-015-1822-2 · Cited by 23 · see citing works

Performance visualization spaces for classification with rejection option

Hanczar, B. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.106984 · Cited by 23 · see citing works

Quantifying the Confidence of Anomaly Detectors in Their Example-Wise Predictions

Perini, L., Vercruyssen, V., & Davis, J. J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-67664-3_14 · Cited by 22 · see citing works

Consistency of plug-in confidence sets for classification in semi-supervised learning

Denis, C., & Hebiri, M. (2019) · Journal of nonparametric statistics · doi:10.1080/10485252.2019.1689241 · Cited by 22 · see citing works

Gradient descent learning of nearest neighbor classifiers with outlier rejection

Urahama, K., & Furukawa, Y. (1995) · Pattern Recognition · doi:10.1016/0031-3203(94)00142-9 · Cited by 22 · see citing works

On the Calibration of Multiclass Classification with RejectionPreprint

Ni, C., Charoenphakdee, N., Honda, J., & Sugiyama, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.10655 · Cited by 21 · see citing works

Reject-Optional LVQ-Based Two-Level Classifier to Improve Reliability in Footstep Identification

Suutala, J., Pirttikangas, S., Riekki, J. P., & Röning, J. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24646-6_12 · Cited by 20 · see citing works

A Classification Approach with a Reject Option for Multi-label Problems

Pillai, I., Fumera, G., & Roli, F. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24085-0_11 · Cited by 19 · see citing works

Interpretable machine learning with reject option

Brinkrolf, J., & Hammer, B. (2018) · at - Automatisierungstechnik · doi:10.1515/auto-2017-0123 · Cited by 18 · see citing works

An Ordinal Data Method for the Classification with Reject Option

Sousa, R., Mora, B., & Cardoso, J. S. (2009) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2009.11 · Cited by 18 · see citing works

Self-Adjusting Reject Options in Prototype Based Classification

Villmann, T., Kaden, M., Bohnsack, A., Villmann, J.-M., Drogies, T., Saralajew, S., & Hammer, B. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-28518-4_24 · Cited by 18 · see citing works

Classification with Rejection Based on Cost-sensitive ClassificationPreprint

Charoenphakdee, N., Cui, Z., Zhang, Y., & Sugiyama, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.11748 · Cited by 18 · see citing works

Reject Strategies Driven Combination of Pattern Classifiers

Mascarilla, L., & Frélicot, C. (2002) · Pattern Analysis and Applications · doi:10.1007/s100440200021 · Cited by 17 · see citing works

Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at Reliable OOD DetectionPreprint

Ulmer, D., & Cinà, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.05329 · Cited by 17 · see citing works

Multi-level Classification of Emphysema in HRCT Lung Images Using Delegated Classifiers

Prasad, M. N., & Sowmya, A. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85988-8_8 · Cited by 17 · see citing works

Multiple classifier combination using reject options and markov fusion networks

Glodek, M., Schels, M., Palm, G., & Schwenker, F. (2012) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2388676.2388778 · Cited by 17 · see citing works

Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart

Pang, T., Zhang, H., He, D., Dong, Y., Su, H., Chen, W. R., Zhu, J., & Liu, T. Y. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01479 · Cited by 17 · see citing works

Bayesian Active Meta-Learning for Reliable and Efficient AI-Based Demodulation

Cohen, K., Park, S., Simeone, O., & Shamai, S. (2021) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2022.3220035 · Cited by 16 · see citing works

Rejection Strategies for Learning Vector Quantization – A Comparison of Probabilistic and Deterministic Approaches

Fischer, L., Nebel, D., Villmann, T., Hammer, B., & Wersing, H. (2014) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-07695-9_10 · Cited by 16 · see citing works

Classification with rejection based on various SVM techniques

Homenda, W., Luckner, M., & Pedrycz, W. (2014) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2014.6889655 · Cited by 16 · see citing works

Mitigating Concept Drift via Rejection

Göpfert, J. P., Hammer, B., & Wersing, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01418-6_45 · Cited by 16 · see citing works

Hyperparameter Optimization

Gridin, I. (2022) · Apress eBooks · doi:10.1007/978-1-4842-8149-9_2 · Cited by 16 · see citing works

Pattern rejection strategies for the design of self-paced EEG-based Brain-Computer Interfaces

Lotte, F., Mouchère, H., & Lécuyer, A. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761454 · Cited by 15 · see citing works

Classification with reject option using contextual information

Condessa, F., Bioucas‐Dias, J. M., Castro, C. A., Ozolek, J. A., & Kovačević, J. (2013) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2013.6556780 · Cited by 15 · see citing works

On Reject and Refine Options in Multicategory Classification

Zhang, C., Wang, W., & Qiao, X. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1282372 · Cited by 15 · see citing works

Transferring the Contamination Factor between Anomaly Detection Domains by Shape Similarity

Perini, L., Vercruyssen, V., & Davis, J. J. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i4.20331 · Cited by 15 · see citing works

Background Check: A General Technique to Build More Reliable and Versatile Classifiers

Perelló-Nieto, M., Filho, T. M. S., Kull, M., & Flach, P. A. (2016) · Proceedings · doi:10.1109/icdm.2016.0150 · Cited by 14 · see citing works

HEARTBEAT CLASSIFICATION USING SUPPORT VECTOR MACHINES (SVMs) WITH AN EMBEDDED REJECT OPTION

ZIDELMAL, Z., AMIROU, A., & Belouchrani, A. (2012) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001412500012 · Cited by 14 · see citing works

A Kernel Based Rejection Method For Supervised Classification

Bounsiar, A., Grall‐Maës, E., & Beauseroy, P. (2007) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1083044 · Cited by 14 · see citing works

Trading off Mistakes and Don’t-Know Predictions

Sayedi, A., Zadimoghaddam, M., & Blum, A. L. (2018) · Figshare · doi:10.1184/r1/6611060 · Cited by 14 · see citing works

A risk bound for ensemble classification with a reject option

Varshney, K. R. (2011) · IEEE Statistical Signal Processing Workshop (SSP) · doi:10.1109/ssp.2011.5967817 · Cited by 14 · see citing works

Supervised Hyperspectral Image Classification With Rejection

Condessa, F., Bioucas‐Dias, J. M., & Kovacevic, J. (2016) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2015.2510032 · Cited by 14 · see citing works

An Optimum Character Recognition System Using Decision Functions

Flores, I. (1958) · IEEE Transactions on Electronic Computers · doi:10.1109/tec.1958.5222530 · Cited by 14 · see citing works

Twin SVM with a reject option through ROC curve

Lin, D., Сун, Л., Toh, K., Zhang, J. B., & Lin, Z. (2017) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2017.05.003 · Cited by 14 · see citing works

Reliable Multilabel Classification: Prediction with Partial Abstention

Nguyen, V.-L., & Hüllermeier, E. (2020) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v34i04.5972 · Cited by 14 · see citing works

COBRASTS: A New Approach to Semi-supervised Clustering of Time Series

Craenendonck, T. V., Meert, W., Dumančić, S., & Blockeel, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01771-2_12 · Cited by 14 · see citing works

Trading off coverage for accuracy in forecasts: Applications to clinical data analysis

Pazzani, M. J., Murphy, P. M., Ali, K., & Schulenburg, D. (1994) · Cited by 13 · see citing works

An optimum class-selective rejection rule for pattern recognition

HA, T. M. (1996) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.1996.546727 · Cited by 13 · see citing works

Reject Options and Confidence Measures for kNN Classifiers

Dalitz, C. (2009) · Cited by 13 · see citing works

Nonparametric Uncertainty Quantification for Single Deterministic Neural Network

Kotelevskii, N., Artemenkov, A., Fedyanin, K., Noskov, F., Fishkov, A., Shelmanov, A., Vazhentsev, A., Petiushko, A., & Panov, M. (2022) · neural information processing systems · doi:10.52202/068431-2631 · Cited by 13 · see citing works

A family of measures for best top-n class-selective decision rules

Capitaine, H. L., & Frélicot, C. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.04.030 · Cited by 12 · see citing works

Local Rejection Strategies for Learning Vector Quantization

Fischer, L., Hammer, B., & Wersing, H. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11179-7_71 · Cited by 12 · see citing works

The data replication method for the classification with reject option

Sousa, R., & Cardoso, J. S. (2013) · AI Communications · doi:10.3233/aic-130566 · Cited by 12 · see citing works

Combination of One-Class Support Vector Machines for Classification with Reject Option

Hanczar, B., & Sébag, M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-44848-9_35 · Cited by 12 · see citing works

A perspective on artificial intelligence: Learning to learn

Bock, P. (1988) · Annals of Operations Research · doi:10.1007/bf02283734 · Cited by 12 · see citing works

A CLASSIFICATION RELIABILITY DRIVEN REJECT RULE FOR MULTI-EXPERT SYSTEMS

Sansone, C., Tortorella, F., & Vento, M. (2001) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001401001210 · Cited by 12 · see citing works

Reliable prediction of anti-diabetic drug failure using a reject option

Kang, S., Cho, S., Rhee, S., & Yu, K. (2016) · Pattern Analysis and Applications · doi:10.1007/s10044-016-0585-4 · Cited by 12 · see citing works

A Model-Agnostic Heuristics for Selective Classification

Pugnana, A., & Ruggieri, S. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i8.26133 · Cited by 12 · see citing works

Estimating the Contamination Factor's Distribution in Unsupervised Anomaly DetectionPreprint

Perini, L., Buerkner, P., & Arto Klami, A. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2210.10487 · Cited by 12 · see citing works

Bounded-abstaining classification for breast tumors in imbalanced ultrasound images

Guan, H., Zhang, Y., Cheng, H.-D., & Tang, X. (2020) · International Journal of Applied Mathematics and Computer Science · doi:10.34768/amcs-2020-0025 · Cited by 11 · see citing works

Probability of default estimation, with a reject option

Coenen, L., Abdullah, A. K. A., & Guns, T. (2020) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa49011.2020.00058 · Cited by 11 · see citing works

A Two-Stage Classifier with Reject Option for Text Categorisation

Fumera, G., Pillai, I., & Roli, F. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27868-9_84 · Cited by 11 · see citing works

On unifying probabilistic/fuzzy and possibilistic rejection-based classifiers

Frélicot, C. (1998) · Lecture notes in computer science · doi:10.1007/bfb0033298 · Cited by 11 · see citing works

General solution and learning method for binary classification with performance constraints

Bounsiar, A., Beauseroy, P., & Grall‐Maës, E. (2008) · Pattern Recognition Letters · doi:10.1016/j.patrec.2008.02.025 · Cited by 11 · see citing works

Rejection Criteria and Pairwise Discrimination of Handwritten Numerals Based on Structural Features

Lou, Z., Liu, K., Yang, J., & Suen, C. Y. (1999) · Pattern Analysis and Applications · doi:10.1007/s100440050031 · Cited by 11 · see citing works

SafePredict: A Meta-Algorithm for Machine Learning That Uses Refusals to Guarantee Correctness

Kocak, M., Ramirez, D., Erkip, E., & Shasha, D. E. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2932415 · Cited by 11 · see citing works

On discriminative learning of prediction uncertainty

Franc, V., & Průša, D. (2019) · International Conference on Machine Learning · Cited by 11 · see citing works

Reliable Classifiers in ROC Space

Vanderlooy, S., Sprinkhuizen-Kuyper, I. G., & Smirnov, E. (2006) · Cited by 11 · see citing works

Optimal strategies for reject option classifiersPreprint

Franc, V., Prusa, D., & Voràček, V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.12523 · Cited by 11 · see citing works

A meta-learning BCI for estimating decision confidence

Tremmel, C., Fernández-Vargas, J., Stamos, D., Cinel, C., Pontil, M., Citi, L., & Poli, R. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac7ba8 · Cited by 11 · see citing works

Optimum tradeoff between class-selective rejection error and average number of classes

HA, T. M. (1997) · Engineering Applications of Artificial Intelligence · doi:10.1016/s0952-1976(97)00034-1 · Cited by 10 · see citing works

Classification with a reject option under Concept Drift: The Droplets algorithm

Loeffel, P.-X., Marsala, C., & Detyniecki, M. (2015) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2015.7344808 · Cited by 10 · see citing works

An Optimum Class-Rejective Decision Rule and Its Evaluation

Capitaine, H. L., & Frélicot, C. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.810 · Cited by 10 · see citing works

Multilabel Classification with Partial Abstention: Bayes-Optimal Prediction under Label Independence

Nguyen, V.-L., & Hüllermeier, E. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12610 · Cited by 10 · see citing works

Can We Trust Fair-AI?

Ruggieri, S., Álvarez, J. M., Pugnana, A., State, L., & Turini, F. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i13.26798 · Cited by 9 · see citing works

An adaptive reject option for LVQ classifiers

Cordella, L. P., Stefano, C. D., Sansone, C., & Vento, M. (1995) · Lecture notes in computer science · doi:10.1007/3-540-60298-4_238 · Cited by 9 · see citing works

A Novel Classification-Rejection Sphere SVMs for Multi-class Classification Problems

Wu, Q., Jia, C., & Chen, W. (2007) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2007.109 · Cited by 9 · see citing works

A Ranking Stability Measure for Quantifying the Robustness of Anomaly Detection Methods

Perini, L., Galvin, C., & Vercruyssen, V. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-65965-3_27 · Cited by 9 · see citing works

An Analysis of Reliable Classifiers through ROC Isometrics

Vanderlooy, S., Sprinkhuizen-Kuyper, I. G., & Smirnov, E. (2006) · Radboud Repository (Radboud University) · Cited by 9 · see citing works

Versatile Verification of Tree EnsemblesPreprint

Devos, L., Meert, W., & Davis, J. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.13880 · Cited by 9 · see citing works

An Empirical Comparison of Ideal and Empirical ROC-Based Reject Rules

Marrocco, C., Molinara, M., & Tortorella, F. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73499-4_5 · Cited by 8 · see citing works

Cost-sensitive Multi-class SVM with Reject Option: A Method for Steam Turbine Generator Fault Diagnosis

Zou, C., Zheng, E., Xu, H., & Chen, L. (2011) · International Journal of Computer Theory and Engineering · doi:10.7763/ijcte.2011.v3.286 · Cited by 8 · see citing works

Optimizing the error/reject trade-off for a multi-expert system using the Bayesian combining rule

Cordella, L. P., Foggia, P., Sansone, C., Tortorella, F., & Vento, M. (1998) · Lecture notes in computer science · doi:10.1007/bfb0033296 · Cited by 8 · see citing works

Enhanced reliability of multilayer perceptron networks through controlled pattern rejection

Vasconcelos, G. C., Fairhurst, M. C., & Bisset, D. L. (1993) · Electronics Letters · doi:10.1049/el:19930179 · Cited by 8 · see citing works

Bootstrap Methods for Reject Rules of Fisher LDA

Xie, J., Qiu, Z., & Wu, J. (2006) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2006.338 · Cited by 8 · see citing works

Accuracy Rejection Normalized-Cost Curves (ARNCCs): A Novel 3-Dimensional Framework for Robust Classification

Abbas, M. R., Nadeem, M. S. A., Shaheen, A., Alshdadi, A. A., Alharbey, R., Shim, S., & Aziz, W. (2019) · IEEE Access · doi:10.1109/access.2019.2950244 · Cited by 8 · see citing works

A Novel Meta Learning Framework for Feature Selection using Data Synthesis and Fuzzy Similarity

Shen, Z., Chen, X., & Garibaldi, J. M. (2020) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz48607.2020.9177769 · Cited by 8 · see citing works

Fast and Accurate $k$-means++ via Rejection SamplingPreprint

Cohen-Addad, V., Lattanzi, S., Norouzi-Fard, A., Sohler, C., & Svensson, O. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.11891 · Cited by 8 · see citing works

Combining Base-Learners into Ensembles

Giraud-Carrier, C. G. (2022) · Cognitive technologies · doi:10.1007/978-3-030-67024-5_9 · Cited by 8 · see citing works

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Lee, I., Ko, H., & Han, D. K. (2002) · Cited by 8 · see citing works

Adversarial Training with Rectified RejectionPreprint

Pang, T., Zhang, H., He, D., Dong, Y., Su, H., Chen, W. R., Zhu, J., & Liu, T. (2021) · arXiv (Cornell University) · Cited by 8 · see citing works

Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection

Villmann, T., Kaden, M., Nebel, D., & Biehl, M. L. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23117-4_66 · Cited by 7 · see citing works

Policy advice and best practices on bias and fairness in AI

Álvarez, J. M., Colmenarejo, A. B., Elobaid, A., Fabbrizzi, S., Fahimi, M., Ferrara, A., Ghodsi, S., Mougan, C., Papageorgiou, I. T., Lobo, P. R., et al. (2024) · Ethics and Information Technology · doi:10.1007/s10676-024-09746-w · Cited by 128 · see citing works

Methodology and evaluation in sports analytics: challenges, approaches, and lessons learned

Davis, J. J., Bransen, L., Devos, L., Jaspers, A., Meert, W., Robberechts, P., Haaren, J. V., & Roy, M. V. (2024) · Machine Learning · doi:10.1007/s10994-024-06585-0 · Cited by 102 · see citing works

Towards Human-AI Teaming to Mitigate Alert Fatigue in Security Operations Centres

Chhetri, M. B., Tariq, S., Singh, R., Jalalvand, F., Paris, C. L., & Nepal‬, ‪. (2024) · ACM Transactions on Internet Technology · doi:10.1145/3670009 · Cited by 70 · see citing works

Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection

Yong, B. X., & Brintrup, A. M. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118196 · Cited by 49 · see citing works

A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities

Tocchetti, A., Corti, L., Balayn, A., Yurrita, M., Lippmann, P., Brambilla, M., & Yang, J. (2024) · ACM Computing Surveys · doi:10.1145/3665926 · Cited by 60 · see citing works

Towards trustworthy cybersecurity operations using Bayesian Deep Learning to improve uncertainty quantification of anomaly detection

Yang, T., Qiao, Y., & Lee, B. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103909 · Cited by 29 · see citing works

A 2 C: A modular multi-stage collaborative decision framework for human–AI teams

Tariq, S., Chhetri, M. B., Nepal‬, ‪., & Paris, C. L. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127318 · Cited by 29 · see citing works

Neighborhood-based credibility anchor learning for universal domain adaptation

Su, W., Han, Z., He, R., Wei, B., He, X., & Yin, Y. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109686 · Cited by 18 · see citing works

Paired contrastive feature for highly reliable offline signature verification

Ji, X., Suehiro, D., & Uchida, S. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109816 · Cited by 18 · see citing works

LAB-Bench: Measuring Capabilities of Language Models for Biology Research

Laurent, J. M., Janizek, J. D., Ruzo, M., Hinks, M. M., Hammerling, M. J., Narayanan, S., Ponnapati, M., White, A. D., & Rodriques, S. G. (2024) · arXiv · doi:10.48550/arxiv.2407.10362 · Cited by 21 · see citing works

U-PASS: An uncertainty-guided deep learning pipeline for automated sleep staging

Heremans, E. R. M., Seedat, N., Buyse, B., Testelmans, D., van der Schaar, M., & Vos, M. D. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108205 · Cited by 16 · see citing works

Agentic Business Process Management: A research manifesto

Calvanese, D., Casciani, A., Giacomo, G. D., Dumas, M., Fournier, F., Kampik, T., Malfa, E. L., Limonad, L., Marrella, A., Metzger, A., et al. (2026) · Information Systems · doi:10.1016/j.is.2026.102738 · Cited by 14 · see citing works

“Even if …” – Diverse Semifactual Explanations of Reject

Artelt, A., & Hammer, B. (2022) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci51031.2022.10022139 · Cited by 13 · see citing works

Conformal selective prediction with cost aware deferral for safe clinical triage under distribution shift

Kwon, H., & Kim, D.-J. (2026) · Scientific Reports · doi:10.1038/s41598-026-40637-w · Cited by 12 · see citing works

Exploring Generalizability of Fine-Tuned Models for Fake News Detection

Suprem, A., Vaidya, S., & Pu, C. (2022) · IEEE International Conference on Collaboration and Internet Computing (CIC) · doi:10.1109/cic56439.2022.00022 · Cited by 12 · see citing works

A Model-Agnostic Heuristics for Selective Classification

Pugnana, A., & Ruggieri, S. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i8.26133 · Cited by 12 · see citing works

“I do not know! but why?” — Local model-agnostic example-based explanations of reject

Artelt, A., Visser, R., & Hammer, B. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126722 · Cited by 12 · see citing works

Safety Monitoring of Machine Learning Perception Functions: A Survey

Ferreira, R., Guerin, J. M., Delmas, K., Guiochet, J., & Waeselynck, H. (2025) · Computational Intelligence · doi:10.1111/coin.70032 · Cited by 18 · see citing works

Survey on Leveraging Uncertainty Estimation Towards Trustworthy Deep Neural Networks: The Case of Reject Option and Post-training Processing

Hasan, T. M., Abdar, M., Khosravi, A., Aickelin, U., Lió, P., Hossain, I., Rahman, A., & Nahavandi, S. (2025) · ACM Computing Surveys · doi:10.1145/3727633 · Cited by 9 · see citing works

Can We Trust Fair-AI?

Ruggieri, S., Álvarez, J. M., Pugnana, A., State, L., & Turini, F. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i13.26798 · Cited by 9 · see citing works

Comparison and benchmark of deep learning methods for non-coding RNA classification

Creux, C., Zehraoui, F., Radvanyi, F., & Tahi, F. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012446 · Cited by 9 · see citing works

Five Degrees of Separation: Investigating the Unexpected Potential of Displaced Human-AI Collaboration Protocols for Apter AI Support

Cabitza, F., Campagner, A., Fregosi, C., Cameli, M., Gallazzi, E., Sconfienza, L. M., & Tontini, G. E. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757601 · Cited by 9 · see citing works

Fast and reliable uncertainty quantification with neural network ensembles for industrial image classification

Thuy, A., & Benoit, D. F. (2024) · Annals of Operations Research · doi:10.1007/s10479-024-06440-4 · Cited by 6 · see citing works

Uncertainty quantification in ordinal classification: A comparison of measures

Haas, S., & Hüllermeier, E. (2025) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2025.109479 · Cited by 6 · see citing works

General scales unlock AI evaluation with explanatory and predictive power

Zhou, L., Pacchiardi, L., Martínez‐Plumed, F., Collins, K. M., Moros-Daval, Y., Zhang, S., Zhao, Q., Huang, Y., Sun, L., Prunty, J. E., et al. (2026) · Nature · doi:10.1038/s41586-026-10303-2 · Cited by 8 · see citing works

A Trustworthiness Score to Evaluate DNN Predictions

Ghobrial, A., Hond, D., Asgari, H., & Eder, K. (2023) · IEEE International Conference on Artificial Intelligence Testing (AITest) · doi:10.1109/aitest58265.2023.00011 · Cited by 6 · see citing works

Model Agnostic Local Explanations of Reject

Artelt, A., Visser, R., & Hammer, B. (2022) · European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) · doi:10.14428/esann/2022.es2022-34 · Cited by 6 · see citing works

The Impact of Artificial Intelligence on Human Resource Management

Maota, T., & Naidoo, V. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-8679-8.ch016 · Cited by 5 · see citing works

Towards explainable evaluation: Explaining predicted performance using local performance regions

Drapal, P., Prudêncio, R. B. C., & Filho, T. M. S. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112351 · Cited by 5 · see citing works

Fuzzy Classifiers with a Two-Stage Reject Option

Nojima, Y., Kawano, K., Shimahara, H., Vernon, E. M., Masuyama, N., & Ishibuchi, H. (2023) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz52849.2023.10309729 · Cited by 5 · see citing works

Assessor Models for Explaining Instance Hardness in Classification Problems

Prudêncio, R. B. C., Lorena, A. C., Filho, T. M. S., Drapal, P., & Valeriano, M. G. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651521 · Cited by 5 · see citing works

Robust ordinal regression for subsets comparisons with interactions

Gilbert, H., Ouaguenouni, M., Öztürk, M., & Spanjaard, O. (2024) · European Journal of Operational Research · doi:10.1016/j.ejor.2024.07.021 · Cited by 4 · see citing works

SCRIB: Set-Classifier with Class-Specific Risk Bounds for Blackbox Models

Lin, Z., Glass, L. M., Westover, M. B., Xiao, C., & Sun, J. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i7.20714 · Cited by 4 · see citing works

Knowledge Amalgamation for Multi-Label Classification via Label Dependency Transfer

Thadajarassiri, J., Hartvigsen, T., Gerych, W., Kong, X., & Rundensteiner, E. A. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i8.26190 · Cited by 4 · see citing works

Adaptive alert prioritisation in security operations centres via learning to defer with human feedback

Jalalvand, F., Chhetri, M. B., Nepal, S., & Paris, C. (2026) · Information Sciences · doi:10.1016/j.ins.2026.124128 · Cited by 3 · see citing works

LCMS-Net: Deep Learning for Raw High Resolution Mass Spectrometry Data Applied to Forensic Cause-of-Death Screening

Menacher, L. M., Ward, L. J., Heintz, F., Gréen, H., & Sysoev, O. (2026) · Analytical Chemistry · doi:10.1021/acs.analchem.5c05404 · Cited by 3 · see citing works

Efficient and robust deep learning for field-level wheat disease classification on resource-constrained devices

Abetu, M. T., Urgessa, T., & Tune, K. K. (2026) · Smart Agricultural Technology · doi:10.1016/j.atech.2026.102091 · Cited by 4 · see citing works

Perception-based constraint solving for sudoku images

Mulamba, M., Mandi, J., Mahmutoğulları, A. İ., & Guns, T. (2024) · Constraints · doi:10.1007/s10601-024-09372-9 · Cited by 3 · see citing works

Abstaining machine learning: philosophical considerations

Schuster, D. (2025) · AI & Society · doi:10.1007/s00146-025-02188-y · Cited by 3 · see citing works

Emotion recognition in Dunhuang Mogao mural figures via PNASNet-CBAM

Wang, S., Liu, S., Jin, M., & Yuan, M. (2025) · npj Heritage Science · doi:10.1038/s40494-025-01961-y · Cited by 3 · see citing works

Failure Prediction in 2D Document Information Extraction with Calibrated Confidence Scores

Kivimäki, J., Lebedev, A., & Nurminen, J. K. (2023) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac57700.2023.00033 · Cited by 3 · see citing works

Automatic Interpretation of Line Probe Assay Test for Tuberculosis

Agrawal, J., Kumar, M., Tiwari, A., Danisetty, S., Dhavala, S. S., Jain, N., Balraj, P., Singh, N., Shingi, S., Kurada, J., et al. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i20.30191 · Cited by 3 · see citing works

Meta-Learning and Novelty Detection for Machine Learning with Reject Option

Drapal, P., Filho, T. M. S., & Prudêncio, R. B. C. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650424 · Cited by 3 · see citing works

Analytical Review of Task Allocation Methods for Human and AI Model Collaboration

Ponomarev, A., & Agafonov, A. (2025) · Informatics and Automation · doi:10.15622/ia.24.1.9 · Cited by 3 · see citing works

Robustness Verification of Multi-Class Tree Ensembles

Devos, L., Cascioli, L., & Davis, J. J. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i19.30093 · Cited by 2 · see citing works

Things Machine Learning Models Know That They Don’t Know

Ruggieri, S., & Pugnana, A. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i27.35094 · Cited by 2 · see citing works

Classification with Reject Option: Distribution-free Error Guarantees via Conformal Prediction

Szabadváry, J. H., Löfström, T., Johansson, U., Sönströd, C., Ahlberg, E., & Carlsson, L. (2025) · Machine Learning with Applications · doi:10.1016/j.mlwa.2025.100664 · Cited by 12 · see citing works

Abstention is all you need

Schönwälder, E., Falkenberg, C., Hartmann, C., & Lehner, W. (2025) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa65442.2025.11248029 · Cited by 2 · see citing works

Enhancing Natura 2000 habitat monitoring: A framework for biodiversity conservation assessment

Agrillo, E., Alessi, N., Angelini, P., Buffi, F., Carli, E., Casella, L., Cutini, M., Filipponi, F., Fratarcangeli, C., Massimi, M., et al. (2025) · Ecological Indicators · doi:10.1016/j.ecolind.2025.114403 · Cited by 2 · see citing works

A lightweight uncertainty modeling approach for wearable sensor signals based on sample overlap estimation

Sun, Y., Li, S. S., Sun, W., Jin, T., Guo, M., Fu, M., Liu, D., & Huang, X. M. (2025) · Measurement · doi:10.1016/j.measurement.2025.120051 · Cited by 2 · see citing works

Deep Learning With Width-Wise Early Exiting and Rejection for Computational Efficient and Trustworthy Modulation Classification

Verbruggen, D., Sallouha, H., & Pollin, S. (2025) · IEEE Transactions on Machine Learning in Communications and Networking · doi:10.1109/tmlcn.2025.3619447 · Cited by 2 · see citing works

Assessment of HeartModel® automated left ventricular ejection fraction for patients with hypertrophic cardiomyopathy

Morrissey, J., Wang, L., Dehn, M. M., Patel, A. R., Rafiq, A., Kadiyala, M., Hughes, M., Rowin, E. J., Maron, M., & Wessler, B. S. (2026) · European Heart Journal - Imaging Methods and Practice · doi:10.1093/ehjimp/qyag003 · Cited by 2 · see citing works

Value-Based Hybrid Intelligence

Sayin, B., Yang, J., Passerini, A., & Casati, F. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230100 · Cited by 2 · see citing works

Assessor Models with Reject Option for soccer result prediction

da Costa, D. C., Prudêncio, R. B. C., & Mota, A. (2023) · doi:10.5753/eniac.2023.234321 · Cited by 2 · see citing works

Assessor Models with a Reject Option for Soccer Result Prediction

da Costa, D. C., Prudêncio, R. B. C., & Mota, A. (2023) · International Conference on Machine Learning and Applications · doi:10.1109/icmla58977.2023.00180 · Cited by 2 · see citing works

A Simple Heuristic for Controlling Human Workload in Learning to Defer

Ponomarev, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78398-2_8 · Cited by 2 · see citing works

Hierarchical Fuzzy Classifier Design Using a Reject Option

Fuerst, R., Masuyama, N., & Nojima, Y. (2024) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee60900.2024.10612164 · Cited by 1 · see citing works

A general supply-inspect cost framework to regulate the reliability-usability trade-offs for few-shot inference

Martínez‐Plumed, F., Jaimovitch-López, G., Ferri, C., Ramírez-Quintana, M. J., & Hernández‐Orallo, J. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01599-6 · Cited by 1 · see citing works

Creating Robust Data Sets for AI by Leveraging Ontological Structures

Vonderhaar, L., Elvira, T., Procko, T., & Ochoa, O. (2025) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc64641.2025.00030 · Cited by 1 · see citing works

Towards Context-sensitive Emotion Recognition

Mukherjee, S. (2025) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3716553.3750824 · Cited by 1 · see citing works

SoK: AI Support for Analyst Situation Awareness in Security Operation Centres

Karunasingha, N., Chhetri, M. B., Nepal‬, ‪., Paris, C. L., & Kanhere, S. S. (2025) · European Symposium on Usable Security (EuroUSEC) · doi:10.1109/eurousec69254.2025.00024 · Cited by 1 · see citing works

Filtering Instances and Rejecting Predictions to Obtain Reliable Models in Healthcare

Valeriano, M. G., Marzagão, D. K., Montelongo, A., Kiffer, C. R. V., Katz, N., & Lorena, A. C. (2026) · Machine Learning · doi:10.1007/s10994-025-06941-8 · Cited by 1 · see citing works

Classification with Reject Option: Building Meta-models to Predict Classification Error

Ueda, P. S. M., Valeriano, M. G., Mangussi, A. D., Prudêncio, R. B. C., & Lorena, A. C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-15987-8_15 · Cited by 1 · see citing works

Aleatoric and Epistemic Uncertainty Measures for Ordinal Classification Through Binary Reduction

Haas, S., & Hüllermeier, E. (2026) · Machine Learning · doi:10.1007/s10994-025-06960-5 · Cited by 1 · see citing works

Near OOD Detection for Vision-Language Prompt Learning with Contrastive Logit Score

Jung, M. C., Dipnall, J. F., Gabbe, B., & Zhao, H. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-026-02760-z · Cited by 1 · see citing works

Big Data and Graph Deep Learning for Financial Decision Support from Social Networks: A Critical Review

Theodorakopoulos, L., & Theodoropoulou, A. (2026) · Electronics · doi:10.3390/electronics15071405 · Cited by 1 · see citing works

Selective classification under imbalance in multiclass settings: A novel metric for bias-aware risk–coverage evaluation

Sağlam, F., Özgen, Ü., Uygun, A., Dinçer, O. S., & Albayrak, C. (2026) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2026.105084 · Cited by 1 · see citing works

Error-Reject Tradeoff Analysis on Two-Stage Classifier Design with a Reject Option

Vernon, E. M., Masuyama, N., & Nojima, Y. (2022) · World Automation Congress (WAC) · doi:10.23919/wac55640.2022.9934284 · Cited by 1 · see citing works

Automatically Inferring the Document Class of a Scientific Article

Gauquier, A., & Senellart, P. P. (2023) · ACM Symposium on Document Engineering (DocEng) · doi:10.1145/3573128.3604894

Exploiting Epistemic Uncertainty at Inference Time for Early-Exit Power Saving

Dymond, J., Stein, S., & Gunn, S. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230323

Applied Data Science for Leasing Score Prediction

Cianci, G., Goglia, R., Guidotti, R., Kapllaj, M., Mosca, R., Pugnana, A., Ricotti, F., & Ruggieri, S. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386651

Soft Dice Confidence: A Near-Optimal Confidence Estimator for Selective Prediction in Semantic Segmentation

Borges, B. L. C., Pacheco, B. M., & Silva, D. (2026) · Machine Learning · doi:10.1007/s10994-026-07096-w

Improving Vehicle Identification Through Advanced Fine-Grained Vehicle Classification

Lima, G. E., Laroca, R., Santos, E. A. P., Nascimento, E., & Menotti, D. (2024) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.5753/sibgrapi.est.2024.31656

Average number of mistakes in sequential risk-averse scenario decision-making

Garatti, S., Carè, A., & Campi, M. C. (2024) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc56724.2024.10885788

Assessor Models for Predicting and Explaining Aleatoric Uncertainty in Classification

Lima, P. B. S., Prudêncio, R. B. C., Lorena, A. C., & Valeriano, M. G. (2024) · doi:10.5753/eniac.2024.245096

An Evaluation Method for Machine Learning Based on Verification and Correction Costs

Kimura, S., Kubota, S., Wada, N., & Hashimoto, K. (2025) · Transactions of the Japanese Society for Artificial Intelligence · doi:10.1527/tjsai.40-3_g-o99

Driven Demand Forecasting in Legacy Qad EnvironmentsPreprint

CHEN, H., GESHE, D., & Wachira, N. N. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5379541

ML-Driven Demand Forecasting in Legacy QAD EnvironmentsPreprint

CHEN, H., GESHE, D., & Wachira, N. N. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5420639

A Trustworthy Model With Uncertainty Management for Predicting Vascular Access Dysfunction in Hemodialysis Patients

Lin, C., Chen, Z. L., Wu, C., Wu, T., & Ma, C. (2025) · IEEE Access · doi:10.1109/access.2025.3612957

A Connection Between Learning to Reject and Bhattacharyya Divergences

Soen, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-03918-7_38

Capacity-aware learning by rejecting complex samples

Khudiyev, A., & Jeannin‐Girardon, A. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.09.254

Machine learning-based prediction of future dementia using routine clinical MRI brain scans and healthcare dataPreprint

Reel, P. S., Al‐Wasity, S., Edwards, C., Reel, S., Mansouri-Benssassi, E., Suveges, S., Mookiah, M. R. K., Krueger, S., Trucco, E., Jefferson, E., et al. (2025) · medRxiv · doi:10.1101/2025.11.12.25340070

Learning to Defer with Scoring Functions

Ponomarev, A. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-4957-3_5

Enhancing Reliability in COVID-19 Classification from CT scans using Learning to Reject

Duong, B., Nguyễn, H. T., Phan, N., & Do, C. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11254549

Machine learning applications for mobile devices

Simacek, D., Panuš, J., Papán, J., & Brídová, I. (2025) · International Conference on Emerging eLearning Technologies and Applications (ICETA) · doi:10.1109/iceta67772.2025.11280216

Learning to Reject: Using an Error Class for More Reliable Object Detection

Puzerenko, A., Borovlev, P., Sukhanov, A. V., & Хатламаджиян, А. Е. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-13615-2_13

Conceptualizing Uncertainty: A Concept-Based Approach to Explaining Uncertainty

Roberts, I., Schulz, A., Schröder, S., Hinder, F., & Hammer, B. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38407-2_20

Towards Visual Navigation Input Monitoring in Autonomous Aerial Vehicles

Kexel, C., & Schütz, M.-F. (2025) · IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace) · doi:10.1109/metroaerospace64938.2025.11114639

Conformalized classifiers with reject option

Johansson, U., & Sönströd, C. (2026) · Machine Learning with Applications · doi:10.1016/j.mlwa.2026.100838

Know What You Don't Know: Selective Prediction for Early Exit DNNs

Bajpai, D. J., & Hanawal, M. K. (2025) · International Conference on AI-ML Systems (AIMLSystems) · doi:10.1109/aimlsystems67835.2025.11330849

MOSAIC: A Structured Multi-level Framework for Probabilistic and Interpretable Cell-type AnnotationPreprint

Yang, M., Qi, J., Lan, M., Huang, J., & Jin, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.31.702798

Increasing the effectiveness of threat detection: machine-learning–based methods for malware classification

Abedalhussain, A. A., & Lyapuntsova, E. V. (2026) · Modelling and Data Analysis · doi:10.17759/mda.2026160101

Characterization of a Reliability Domain for Image Classifiers

Coz, A. L. (2024) · doi:10.70675/b6f39077z9dc3z4edcz9e4cz073207f40c00

Deep learning algorithms for the identification, classification and prediction of the function of non-coding RNAs in bladder cancer

Creux, C. (2024) · doi:10.70675/6b1e75aeze0faz4027zac61z8bbd43434d69

Towards Reliable Time Series Forecasting Under Future Uncertainty: Ambiguity and Novelty Rejection Mechanisms

Feng, N., Lai, S., Zou, X., Yang, J., 冯坤龙, Zhou, F., Yue, Y., Chu, Z., Chen, X., Chieng, D. H. T., et al. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464741

Risk-controlled adaptive local calibration for NIRS-based nutrient prediction in liquid organic manure

Friedrich, L., & Krieger, K.-L. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2026.111815

Predicting future dementia from routine clinical MRI and linked healthcare data

Reel, P. S., Al‐Wasity, S., Edwards, C., Reel, S., Mansouri-Benssassi, E., Suveges, S., Mookiah, M. R. K., Krueger, S., Trucco, E., Jefferson, E., et al. (2026) · Alzheimer s Research & Therapy · doi:10.1186/s13195-026-02079-4

Hybrid regression–classification framework with probability-guided decision refinement for green tea quality assessment using electronic nose

Handayani, R., Sarno, R., Wijaya, D. R., Fajar, A., Haryono, A. T., & Lee, S.-S. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3890

Integrating texture features with hierarchical machine learning classifiers for few shot stored grain pest identification

Yu, S., Xu, L., Cheng, H., Tian, C., Hang, Q., Zhao, S., PeiAn, T. A. N. G., Ding, C., & Liu, Q. (2026) · Journal of Stored Products Research · doi:10.1016/j.jspr.2026.103115

Learning Selective Deferral Policies for Reliable Medical Text Classification

Albalawi, T., & Alzahrani, A. (2026) · Technologies · doi:10.3390/technologies14060359

The Science of Rejection: A Research Area for Human Computation

Sayin, B., Passerini, A., Casati, F., & Yang, J. (2021) · Institutional Research Information System (Università degli Studi di Trento)

Classification with Reject Option: Distribution-free Error Guarantees via Conformal Prediction

Szabadváry, J. H., Löfström, T., Johansson, U., Sönströd, C., Ahlberg, E., & Carlsson, L. (2025) · Machine Learning with Applications · doi:10.1016/j.mlwa.2025.100664 · Cited by 12 · see citing works

Reject Options for Incremental Regression Scenarios

Jakob, J., Hasenjäger, M., & Hammer, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15937-4_21 · Cited by 1 · see citing works

Meta-Learning and Novelty Detection for Machine Learning with Reject Option

Drapal, P., Filho, T. M. S., & Prudêncio, R. B. C. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650424 · Cited by 3 · see citing works

Things Machine Learning Models Know That They Don’t Know

Ruggieri, S., & Pugnana, A. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i27.35094 · Cited by 2 · see citing works