The DOI matches its Crossref record; 245 of the 252 references it cites were found in a registry or an index.
Machine learning with a reject option: a survey
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“In order to assess models' ability to refrain from answering a question when uncertain [14], we also include an additional multiple choice answer for each question:”
From LAB-Bench: Measuring Capabilities of Language Models for Biology Research (2024) · cites this paper as [14]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(1996) · Cited by 20,179
Advances in neural information processing systems 7
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,759
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
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
(2009) · Minds at UW (University of Wisconsin) · Cited by 4,791
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
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
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
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
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
Research works printed but not linked: 13
- Alhoniemi, E., Himberg, J., & Vesanto, J. (1999). Probabilistic measures for responses of self-organizing map units. In H. Bothe, E. Oja, E. Massad and C. Haefke, (eds.), Proceedings of the international ICSC congress on computational intelligence methods and applications. ICSC Academic Press, Zurich, Switzerland, (pp. 286–290). ICSC Academic Press.
- Cao, Y., Cai, T., & An, B. (2022). Generalizing consistent multi-class classification with rejection to be compatible with arbitrary losses. (NeurIPS), (pp. 1–14).
- Ferri, C., & Hernández-Orallo, J. (2004). Cautious lassifiers. Proceedings of ROC analysis in artificial intelligence, 1st international workshop (ROCAI-2004), (pp. 27–36).
- Frélicot, C. (1997). Learning rejection thresholds for a class of fuzzy classifiers from possibilistic clustered noisy data. In 7th International fuzzy systems association world congress, IFSA, (vol. 3, pp. 111–116).
- Friedel, C. C., Rückert, U., & Kramer, S. (2006). Cost Curves for Abstaining Classifiers. In ICML 2006 - 3th workshop ROC analysis in ML, Pittsburgh, Pennsylvania.
- Hansen, L. K., Liisberg, C., & Salamon, P. (1997). The error-reject tradeoff. Open Systems & Information Dynamics, 4(2), 159–184. · DOI
- Landgrebe, T. C., Tax, D. M., Paclík, P., Duin, R. P., & Andrew, C. (2004). A combining strategy for ill-defined problems. In Proceedings of the 15th annual symposium of the pattern recognition association of South Africa, (pp. 57–62).
- Lee, J. K., Bu, Y., Rajan, D., Sattigeri, P., Panda, R., Das, S., & Wornell, G. W. (2021). Fair selective classification via sufficiency. In Proceedings of the 38th international conference on machine learning, (vol. 139, pp. 6076–6086).
- Nalisnick, E., Matsukawa, A., Whye, Y., Dilan, T., & Balaji, G. (2019). Hybrid models with deep and invertible features (ICML, 2019).
- Prasad, N. R., Almanza-Garcia, S., & Lu, T. T. (2009). Anomaly detection. Computers, Materials and Continua, 14(1), 1–22.
- Van der Plas, D., Meert, W., Verbraecken, J., & Davis, J. (2021). A reject option for automated sleep stage scoring. In ICML workshop on interpretable machine learning in healthcare, Virtual event.
- Vanschoren, J. (2018). Meta-learning: A survey. pp. 1–29.
- Zhou, L., Martínez-Plumed, F., Hernández-Orallo, J., Ferri, C., & Schellaert, W. (2022). Reject before you run: Small assessors anticipate big language models. In CEUR workshop proceedings, (vol. 3169).
Cited by
Sorted by citations
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 147
Methodology and evaluation in sports analytics: challenges, approaches, and lessons learned
Davis, J., Bransen, L., Devos, L., Jaspers, A., Meert, W., Robberechts, P., Haaren, J. V., & Roy, M. V. (2024) · Machine-mediated learning · doi:10.1007/s10994-024-06585-0 · Cited by 94
Self-citationTowards 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
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 55
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 77
Yang, T., Qiao, Y., & Lee, B. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103909 · Cited by 29
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 34
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
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
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
“In order to assess models’ ability to refrain from answering a question when uncertain [14], we also include an additional multiple choice answer for each question: Insufficient information to answer the question.”
BackgroundU-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
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 27
“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
Kwon, H., & Kim, D.-J. (2026) · Scientific Reports · doi:10.1038/s41598-026-40637-w · Cited by 12
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 14
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 18
“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 16
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 23
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 17
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 11
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
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
Thuy, A., & Benoit, D. F. (2024) · Annals of Operations Research · doi:10.1007/s10479-024-06440-4 · Cited by 6
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 7
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 21
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
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 8
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
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
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
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
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
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
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
Jalalvand, F., Chhetri, M. B., Nepal, S., & Paris, C. (2026) · Information Sciences · doi:10.1016/j.ins.2026.124128 · Cited by 3
Menacher, L. M., Ward, L. J., Heintz, F., Gréen, H., & Sysoev, O. (2026) · Analytical Chemistry · doi:10.1021/acs.analchem.5c05404 · Cited by 3
Abetu, M. T., Urgessa, T., & Tune, K. K. (2026) · Smart Agricultural Technology · doi:10.1016/j.atech.2026.102091 · Cited by 4
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 5
Abstaining machine learning: philosophical considerations
Schuster, D. (2025) · AI & Society · doi:10.1007/s00146-025-02188-y · Cited by 6
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
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 4
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
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
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
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
Self-citationThings 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
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
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 3
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
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
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
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
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
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
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
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
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
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
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
Towards Context-sensitive Emotion Recognition
Mukherjee, S. (2025) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3716553.3750824 · Cited by 1
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
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
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
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 4
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 3
Theodorakopoulos, L., & Theodoropoulou, A. (2026) · Electronics · doi:10.3390/electronics15071405 · Cited by 1
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
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
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
Borges, B. L. C., Pacheco, B. M., & Silva, D. (2026) · Machine Learning · doi:10.1007/s10994-026-07096-w · Cited by 4
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
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
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 · Cited by 1
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
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) · theses.fr (ABES) · doi:10.70675/b6f39077z9dc3z4edcz9e4cz073207f40c00
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
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
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
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
Metadata
- Authors
- Hendrickx, K., Perini, L., Van der Plas, D., Meert, W., & Davis, J.
- Affiliations
- Siemens (Belgium) (BE)
- KU Leuven (BE)
- Corresponding author
- Hendrickx, Kilian
- Venue
- Machine Learning
- Published
- 2024-03-29
- Volume
- 113
- Issue
- 5
- Pages
- 3073-3110
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W3183516990
- Cited by
- 134
- References
- 252
- Citations by year
- 2026: 59 · 2025: 31 · 2024: 23
- FWCI
- 16.68
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- arXiv (Cornell University) (cc-by)
- Funders
- agentschap innoveren en ondernemen (HBC.2017.0226, HBC.2019.2615)
- onderzoeksraad, ku leuven (C14/17/070)
- vlaamse overheid (Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- abstention, decision support
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
