Statistical Comparisons of Classifiers over Multiple Data Sets

The use of ranks to avoid the assumption of normality implicit in the analysis of variance

Friedman, M. (1937) · Journal of the American Statistical Association · doi:10.1080/01621459.1937.10503522 · Cited by 7,005

The earth is round (p < .05).

Cohen, J. (1994) · American Psychologist · doi:10.1037/0003-066x.49.12.997 · Cited by 4,003

A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings

Friedman, M. (1940) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177731944 · Cited by 2,785

The pascal visual object classes (VOC) challenge

Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599

A systematic analysis of performance measures for classification tasks

Sokolova, M., & Lapalme, G. (2009) · Information Processing & Management · doi:10.1016/j.ipm.2009.03.002 · Cited by 7,515

The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

Chicco, D., & Jurman, G. (2020) · BMC Genomics · doi:10.1186/s12864-019-6413-7 · Cited by 6,143

A survey on concept drift adaptation

Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014) · ACM Computing Surveys · doi:10.1145/2523813 · Cited by 3,735

Deep learning for time series classification: a review

Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A. (2019) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-019-00619-1 · Cited by 3,342

Machine learning and deep learning

Janiesch, C., Zschech, P., & Heinrich, K. (2021) · Electronic Markets · doi:10.1007/s12525-021-00475-2 · Cited by 2,959

Untitled work

(2010) · doi:10.5555/1756006.1859921 · Cited by 1,924

Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation

Rodriguez, J. D., Perez, A., & Lozano, J. A. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.187 · Cited by 1,890

Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation

Sokolova, M., Japkowicz, N., & Szpakowicz, S. (2006) · Lecture Notes in Computer Science · doi:10.1007/11941439_114 · Cited by 1,974

Inceptiontime: finding alexnet for time series classification

Ismail Fawaz, H., Lucas, B., Forestier, G., Pelletier, C., Schmidt, D. F., Weber, J., Webb, G. I., Idoumghar, L., Muller, P.-A., & Petitjean, F. (2020) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-020-00710-y · Cited by 1,788

The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances

Bagnall, A., Lines, J., Bostrom, A., Large, J., & Keogh, E. (2017) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-016-0483-9 · Cited by 1,377

Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE

Douzas, G., Bacao, F., & Last, F. (2018) · Information Sciences · doi:10.1016/j.ins.2018.06.056 · Cited by 1,277

Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research

Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015) · European Journal of Operational Research · doi:10.1016/j.ejor.2015.05.030 · Cited by 1,284

Big data deep learning: Challenges and perspectives

Chen, X.-W., & Lin, X. (2014) · IEEE Access · doi:10.1109/access.2014.2325029 · Cited by 1,255

High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning

Erfani, S. M., Rajasegarar, S., Karunasekera, S., & Leckie, C. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.03.028 · Cited by 1,162

The UCR time series archive

Dau, H. A., Bagnall, A., Kamgar, K., Yeh, C.-C. M., Zhu, Y., Gharghabi, S., Ratanamahatana, C. A., & Keogh, E. (2019) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2019.1911747 · Cited by 1,128

Reliable Accuracy Estimates from k-Fold Cross Validation

Wong, T.-T., & Yeh, P.-Y. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2912815 · Cited by 1,074

Obtaining well calibrated probabilities using Bayesian binning

Pakdaman Naeini, M., Cooper, G., & Hauskrecht, M. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9602 · Cited by 991

Learning under Concept Drift: A Review

Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., & Zhang, G. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2876857 · Cited by 1,603

Cross-validation failure: Small sample sizes lead to large error bars

Varoquaux, G. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.061 · Cited by 897

TS2Vec: Towards Universal Representation of Time Series

Yue, Z., Wang, Y., Duan, J., Yang, T., Huang, C., Tong, Y., & Xu, B. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i8.20881 · Cited by 850

Predictive performance of presence‐only species distribution models: a benchmark study with reproducible code

Valavi, R., Guillera‐Arroita, G., Lahoz‐Monfort, J. J., & Elith, J. (2022) · Ecological Monographs · doi:10.1002/ecm.1486 · Cited by 830

Multiple instance learning: A survey of problem characteristics and applications

Carbonneau, M.-A., Cheplygina, V., Granger, E., & Gagnon, G. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.009 · Cited by 740

Untitled work

(2011) · Cited by 675

Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology

Tellez, D., Litjens, G., Bándi, P., Bulten, W., Bokhorst, J.-M., Ciompi, F., & van der Laak, J. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101544 · Cited by 649

Evaluating multiple classifiers for stock price direction prediction

Ballings, M., Van den Poel, D., Hespeels, N., & Gryp, R. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.05.013 · Cited by 611

An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition

Tsatsaronis, G., Balikas, G., Malakasiotis, P., Partalas, I., Zschunke, M., Alvers, M. R., Weissenborn, D., Krithara, A., Petridis, S., Polychronopoulos, D., et al. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0564-6 · Cited by 598

Overview of Friedman’s test and post-hoc analysis

Pereira, D. G., Afonso, A., & Medeiros, F. M. (2015) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2014.931971 · Cited by 585

Classification of time series by shapelet transformation

Hills, J., Lines, J., Baranauskas, E., Mapp, J., & Bagnall, A. (2014) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-013-0322-1 · Cited by 538

Handcrafted vs. non-handcrafted features for computer vision classification

Nanni, L., Ghidoni, S., & Brahnam, S. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.05.025 · Cited by 542

Fairness in machine learning: A survey

Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646

New insights into churn prediction in the telecommunication sector: a profit driven data mining approach

Verbeke, W., Dejaeger, K., Martens, D., Hur, J., & Baesens, B. (2012) · European Journal of Operational Research · doi:10.1016/j.ejor.2011.09.031 · Cited by 462

An up-to-date comparison of state-of-the-art classification algorithms

Zhang, C., Liu, C., Zhang, X., & Almpanidis, G. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.04.003 · Cited by 440

A Comparison of MCC and CEN Error Measures in Multi-Class Prediction

Jurman, G., Riccadonna, S., & Furlanello, C. (2012) · PLoS ONE · doi:10.1371/journal.pone.0041882 · Cited by 406

Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study

Liang, D., Lu, C.-C., Tsai, C.-F., & Shih, G.-A. (2016) · European Journal of Operational Research · doi:10.1016/j.ejor.2016.01.012 · Cited by 405

Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models

Ghotra, B., McIntosh, S., & Hassan, A. E. (2015) · 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering · doi:10.1109/icse.2015.91 · Cited by 364

Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles

Lines, J., Taylor, S., & Bagnall, A. (2018) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3182382 · Cited by 333

Comparing oversampling techniques to handle the class imbalance problem: a customer churn prediction case study

Amin, A., Anwar, S., Adnan, A., Nawaz, M., Howard, N., Qadir, J., Hawalah, A., & Hussain, A. (2016) · IEEE Access · doi:10.1109/access.2016.2619719 · Cited by 310

Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion

Jabeen, K., Khan, M. A., Alhaisoni, M., Tariq, U., Zhang, Y.-D., Hamza, A., Mickus, A., & Damaševičius, R. (2022) · Sensors · doi:10.3390/s22030807 · Cited by 314

EuroCity Persons: A Novel Benchmark for Person Detection in Traffic Scenes

Braun, M., Krebs, S., Flohr, F., & Gavrila, D. M. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2897684 · Cited by 307

Modelling species presence‐only data with random forests

Valavi, R., Elith, J., Lahoz‐Monfort, J. J., & Guillera‐Arroita, G. (2021) · Ecography · doi:10.1111/ecog.05615 · Cited by 307

Measure it? Manage it? Ignore it? software practitioners and technical debt

Ernst, N. A., Bellomo, S., Ozkaya, I., Nord, R. L., & Gorton, I. (2015) · Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering · doi:10.1145/2786805.2786848 · Cited by 260

Using VADER sentiment and SVM for predicting customer response sentiment

Borg, A., & Boldt, M. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113746 · Cited by 251

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

Shain, C., Blank, I. A., van Schijndel, M., Schuler, W., & Fedorenko, E. (2020) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.107307 · Cited by 215

A review of the F-measure: its history, properties, criticism, and alternatives

Christen, P., Hand, D. J., & Kirielle, N. (2024) · ACM Computing Surveys · doi:10.1145/3606367 · Cited by 236

BioPatRec: A modular research platform for the control of artificial limbs based on pattern recognition algorithms

Ortiz-Catalan, M., Brånemark, R., & Håkansson, B. (2013) · Source Code for Biology and Medicine · doi:10.1186/1751-0473-8-11 · Cited by 232

Data mining techniques for software effort estimation: a comparative study

Dejaeger, K., Verbeke, W., Martens, D., & Baesens, B. (2012) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2011.55 · Cited by 224

A systematic comparison of supervised classifiers

Amancio, D. R., Comin, C. H., Casanova, D., Travieso, G., Bruno, O. M., Rodrigues, F. A., & da Fontoura Costa, L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094137 · Cited by 225

Seizure Classification From EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System

Jiang, Y., Wu, D., Deng, Z., Qian, P., Wang, J., Wang, G., Chung, F.-L., Choi, K.-S., & Wang, S. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2017.2748388 · Cited by 221

Developing a practical suicide risk prediction model for targeting high‐risk patients in the Veterans health Administration

Kessler, R. C., Hwang, I., Hoffmire, C. A., McCarthy, J. F., Petukhova, M. V., Rosellini, A. J., Sampson, N. A., Schneider, A. L., Bradley, P. A., Katz, I. R., et al. (2017) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.1575 · Cited by 209

A comparative study of classifier ensembles for bankruptcy prediction

Tsai, C.-F., Hsu, Y.-F., & Yen, D. C. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2014.08.047 · Cited by 202

Explainable decision forest: Transforming a decision forest into an interpretable tree

Sagi, O., & Rokach, L. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.03.013 · Cited by 189

Analyzing Data Granularity Levels for Insider Threat Detection Using Machine Learning

Le, D. C., Zincir-Heywood, N., & Heywood, M. I. (2020) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2020.2967721 · Cited by 191

Decision trees for uplift modeling with single and multiple treatments

Rzepakowski, P., & Jaroszewicz, S. (2012) · Knowledge and Information Systems · doi:10.1007/s10115-011-0434-0 · Cited by 181

ShapeNet: A Shapelet-Neural Network Approach for Multivariate Time Series Classification

Li, G., Choi, B., Xu, J., S Bhowmick, S., Chun, K.-P., & Wong, G. L.-H. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i9.17018 · Cited by 169

Hi-BEHRT: Hierarchical Transformer-Based Model for Accurate Prediction of Clinical Events Using Multimodal Longitudinal Electronic Health Records

Li, Y., Mamouei, M., Salimi-Khorshidi, G., Rao, S., Hassaine, A., Canoy, D., Lukasiewicz, T., & Rahimi, K. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3224727 · Cited by 156

Anomaly Detection for Insider Threats Using Unsupervised Ensembles

Le, D. C., & Zincir-Heywood, N. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3071928 · Cited by 147

FERAtt: Facial Expression Recognition With Attention Net

Fernandez, P. D. M., Pena, F. A. G., Ren, T. I., & Cunha, A. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00112 · Cited by 142

Multivariate pattern classification reveals autonomic and experiential representations of discrete emotions.

Kragel, P. A., & LaBar, K. S. (2013) · Emotion · doi:10.1037/a0031820 · Cited by 134

LITNET-2020: An Annotated Real-World Network Flow Dataset for Network Intrusion Detection

Damasevicius, R., Venckauskas, A., Grigaliunas, S., Toldinas, J., Morkevicius, N., Aleliunas, T., & Smuikys, P. (2020) · Electronics · doi:10.3390/electronics9050800 · Cited by 132

Fairness in Machine Learning: A Survey

Caton, S., & Haas, C. (2020) · arXiv · doi:10.48550/arxiv.2010.04053 · Cited by 646

Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text

Gehrmann, S., Clark, E., & Sellam, T. (2023) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13715 · Cited by 123

Beyond sigmoids: How to obtain well-calibrated probabilities from binary classifiers with beta calibration

Kull, M., Silva Filho, T. M., & Flach, P. (2017) · Electronic Journal of Statistics · doi:10.1214/17-ejs1338si · Cited by 111

Deep Neural Network Ensembles for Time Series Classification

Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A. (2019) · 2019 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852316 · Cited by 103

PMC: A Privacy-preserving Deep Learning Model Customization Framework for Edge Computing

Liu, B., Li, Y., Liu, Y., Guo, Y., & Chen, X. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3432208 · Cited by 95

Sample Subset Optimization Techniques for Imbalanced and Ensemble Learning Problems in Bioinformatics Applications

Yang, P., Yoo, P. D., Fernando, J., Zhou, B. B., Zhang, Z., & Zomaya, A. Y. (2014) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2013.2257480 · Cited by 90

Convolutional neural networks for decoding of covert attention focus and saliency maps for EEG feature visualization

Farahat, A., Reichert, C., Sweeney-Reed, C. M., & Hinrichs, H. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab3bb4 · Cited by 92

Selecting Representative Data Sets

Borovicka, T., Jirina, M., Kordik, P., & Jiri, M. (2012) · Advances in Data Mining Knowledge Discovery and Applications · doi:10.5772/50787 · Cited by 94

High precision protein functional site detection using 3D convolutional neural networks

Torng, W., & Altman, R. B. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty813 · Cited by 69

Protecting against evaluation overfitting in empirical reinforcement learning

Whiteson, S., Tanner, B., Taylor, M. E., & Stone, P. (2011) · 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL) · doi:10.1109/adprl.2011.5967363 · Cited by 86

With Little Power Comes Great Responsibility

Card, D., Henderson, P., Khandelwal, U., Jia, R., Mahowald, K., & Jurafsky, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.745 · Cited by 84

Rule extraction in unsupervised anomaly detection for model explainability: Application to OneClass SVM

Barbado, A., Corcho, Ó., & Benjamins, R. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116100 · Cited by 77

Improving activity recognition using a wearable barometric pressure sensor in mobility-impaired stroke patients

Massé, F., Gonzenbach, R. R., Arami, A., Paraschiv-Ionescu, A., Luft, A. R., & Aminian, K. (2015) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-015-0060-2 · Cited by 77

From classification to quantification in tweet sentiment analysis

Gao, W., & Sebastiani, F. (2016) · Social Network Analysis and Mining · doi:10.1007/s13278-016-0327-z · Cited by 75

Screening nonrandomized studies for medical systematic reviews: a comparative study of classifiers

Bekhuis, T., & Demner-Fushman, D. (2012) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2012.05.002 · Cited by 70

Calibrating machine learning approaches for probability estimation: A comprehensive comparison

Ojeda, F. M., Jansen, M. L., Thiéry, A., Blankenberg, S., Weimar, C., Schmid, M., & Ziegler, A. (2023) · Statistics in Medicine · doi:10.1002/sim.9921 · Cited by 71

On the appropriateness of Platt scaling in classifier calibration

Böken, B. (2021) · Information Systems · doi:10.1016/j.is.2020.101641 · Cited by 70

The effect of ligand-based tautomer and protomer prediction on structure-based virtual screening.

Kalliokoski, T., Salo, H. S., Lahtela-Kakkonen, M., & Poso, A. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci900364w · Cited by 66

A decomposition of the outlier detection problem into a set of supervised learning problems

Paulheim, H., & Meusel, R. (2015) · Machine Learning · doi:10.1007/s10994-015-5507-y · Cited by 62

Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets

Dror, R., Baumer, G., Bogomolov, M., & Reichart, R. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00074 · Cited by 63

Ensemble Deep Learning for Biomedical Time Series Classification

Jin, L.-P., & Dong, J. (2016) · Computational Intelligence and Neuroscience · doi:10.1155/2016/6212684 · Cited by 60

Interpretable generalized additive neural networks

Kraus, M., Tschernutter, D., Weinzierl, S., & Zschech, P. (2024) · European Journal of Operational Research · doi:10.1016/j.ejor.2023.06.032 · Cited by 59

An application of univariate and multivariate approaches in fMRI to quantifying the hemispheric lateralization of acoustic and linguistic processes

McGettigan, C., Evans, S., Rosen, S., Agnew, Z. K., Shah, P., & Scott, S. K. (2012) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00161 · Cited by 56

A systematic deep learning model selection for p300-based brain-computer interfaces

Abibullaev, B., & Zollanvari, A. (2022) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2021.3051136 · Cited by 53

Fast instance selection for speeding up support vector machines

Chen, J., Zhang, C., Xue, X., & Liu, C.-L. (2013) · Knowledge-Based Systems · doi:10.1016/j.knosys.2013.01.031 · Cited by 53

Spectro-Temporal Sparsity Characterization for Dysarthric Speech Detection

Kodrasi, I., & Bourlard, H. (2020) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2020.2985066 · Cited by 52

Learning hidden patterns from patient multivariate time series data using convolutional neural networks: A case study of healthcare cost prediction

Morid, M. A., Sheng, O. R. L., Kawamoto, K., & Abdelrahman, S. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103565 · Cited by 49

A deep learning model for Alzheimer’s disease diagnosis based on patient clinical records

Ávila-Jiménez, J. L., Cantón-Habas, V., Carrera-González, M. D. P., Rich-Ruiz, M., & Ventura, S. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107814 · Cited by 50

Getting a CLUE: A Method for Explaining Uncertainty Estimates

Antorán, J., Bhatt, U., Adel, T., Weller, A., & Hernández-Lobato, J. M. (2021) · Apollo - University of Cambridge Repository · doi:10.17863/cam.69929 · Cited by 49

The Benchmark Lottery

Dehghani, M., Tay, Y., Gritsenko, A. A., Zhao, Z., Houlsby, N., Diaz, F., Metzler, D., & Vinyals, O. (2021) · arXiv · doi:10.48550/arxiv.2107.07002 · Cited by 48

On the effect of calibration in classifier combination

Bella, A., Ferri, C., Hernández-Orallo, J., & Ramírez-Quintana, M. J. (2013) · Applied Intelligence · doi:10.1007/s10489-012-0388-2 · Cited by 45

Shortlisting machine learning-based stock trading recommendations using candlestick pattern recognition

Cagliero, L., Fior, J., & Garza, P. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119493 · Cited by 46

Detecting emergencies in patient portal messages using large language models and knowledge graph-based retrieval-augmented generation

Liu, S., Wright, A. P., McCoy, A. B., Huang, S. S., Steitz, B., & Wright, A. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf059 · Cited by 44

Binary Classifier Calibration Using an Ensemble of Near Isotonic Regression Models

Naeini, M. P., & Cooper, G. F. (2016) · 2016 IEEE 16th International Conference on Data Mining (ICDM) · doi:10.1109/icdm.2016.0047 · Cited by 46

EDLA-EFDS: A Novel Ensemble Deep Learning Approach For Electrical Fault Detection Systems

Elmasry, W., & Wadi, M. (2022) · Electric Power Systems Research · doi:10.1016/j.epsr.2022.107834 · Cited by 42

What's in a p-value in NLP?

Søgaard, A., Johannsen, A., Plank, B., Hovy, D., & Martínez Alonso, H. (2014) · Proceedings of the Eighteenth Conference on Computational Natural Language Learning · doi:10.3115/v1/w14-1601 · Cited by 39

IENet: a robust convolutional neural network for EEG based brain-computer interfaces

Du, Y., & Liu, J. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac7257 · Cited by 34

Learning Representations for Incomplete Time Series Clustering

Ma, Q., Chen, C., Li, S., & Cottrell, G. W. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i10.17070 · Cited by 34

Interpersonal heart rate synchrony predicts effective information processing in a naturalistic group decision-making task

Sharika, K. M., Thaikkandi, S., Nivedita, & Platt, M. L. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2313801121 · Cited by 33

A machine learning framework for interpretable predictions in patient pathways: the case of predicting ICU admission for patients with symptoms of sepsis

Zilker, S., Weinzierl, S., Kraus, M., Zschech, P., & Matzner, M. (2024) · Health Care Management Science · doi:10.1007/s10729-024-09673-8 · Cited by 29