Statlog (German Credit Data)

Accurate predictions on small data with a tabular foundation model

Hollmann, N., Müller, S., Purucker, L., Krishnakumar, A., Körfer, M., Hoo, S. B., Schirrmeister, R. T., & Hutter, F. (2025) · Nature · doi:10.1038/s41586-024-08328-6 · Cited by 1,427

Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review

Verma, S., Boonsanong, V., Hoang, M., Hines, K., Dickerson, J. P., & Shah, C. S. (2024) · ACM Computing Surveys · doi:10.1145/3677119 · Cited by 723

Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review

Verma, S., Boonsanong, V., Hoang, M., Hines, K. E., Dickerson, J. P., & Shah, C. (2020) · arXiv · doi:10.48550/arxiv.2010.10596 · Cited by 267

Financial fraud detection through the application of machine learning techniques: a literature review

Aros, L. H., Molano, L. X. B., Gutiérrez-Portela, F., Hernandez, J. J. M., & Barrero, M. S. R. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03606-0 · Cited by 217

Fairness issues, current approaches, and challenges in machine learning models

Jui, T. D., & Rivas, P. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-02083-2 · Cited by 100

Probabilistic Confusion Matrix: A Novel Method for Machine Learning Algorithm Generalized Performance Analysis

Markoulidakis, I., & Markoulidakis, G. (2024) · Technologies · doi:10.3390/technologies12070113 · Cited by 96

Enhancing transparency and fairness in automated credit decisions: an explainable novel hybrid machine learning approach

Nwafor, C. N., Nwafor, O., & Brahma, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-75026-8 · Cited by 57

Mathematical Modeling and Analysis of Credit Scoring Using the LIME Explainer: A Comprehensive Approach

Aljadani, A., Alharthi, B., Farsi, M. A., Balaha, H. M., Badawy, M., & Elhosseini, M. A. (2023) · Mathematics · doi:10.3390/math11194055 · Cited by 55

FinTral: A Family of GPT-4 Level Multimodal Financial Large Language Models

Bhatia, G., Nagoudi, E. M. B., Cavusoglu, H., & Abdul-Mageed, M. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.774 · Cited by 53

Accuracy Comparison between Five Machine Learning Algorithms for Financial Risk Evaluation

Dong, H., Liu, R., & Tham, A. W. (2024) · Journal of risk and financial management · doi:10.3390/jrfm17020050 · Cited by 46

Algorithmic decision making methods for fair credit scoring

Moldovan, D. (2023) · IEEE Access · doi:10.1109/access.2023.3286018 · Cited by 28

Comparative Study of sEMG Feature Evaluation Methods Based on the Hand Gesture Classification Performance

Hellara, H., Barioul, R., Sahnoun, S., Fakhfakh, A., & Kanoun, O. (2024) · Sensors · doi:10.3390/s24113638 · Cited by 28

US-LIME: Increasing fidelity in LIME using uncertainty sampling on tabular data

Saadatfar, H., Kiani-Zadegan, Z., & Ghahremani-Nezhad, B. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127969 · Cited by 22

Accurate Fairness: Improving Individual Fairness without Trading Accuracy

Li, X., Wu, P., & Su, J. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26674 · Cited by 26

A practical evaluation of AutoML tools for binary, multiclass, and multilabel classification

Aragão, M. V. C., Afonso, A. G., Ferraz, R. C., Ferreira, R. G., Leite, S. G., de Figueiredo, F. A. P., & Mafra, S. B. (2025) · Scientific Reports · doi:10.1038/s41598-025-02149-x · Cited by 23

Best practices for responsible machine learning in credit scoring

Valdrighi, G., Ribeiro, A. M., Pereira, J. S. B., Guardieiro, V., Hendricks, A., Filho, D. M., Garcia, J. D. N., Bocca, F. F., Veronese, T. B., Wanner, L., et al. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11520-y · Cited by 24

Modeling of extended osprey optimization algorithm with Bayesian neural network: An application on Fintech to predict financial crisis

Abdullayev, I., Akhmetshin, E., Kosorukova, I. V., Klochko, E., Cho, W., & Joshi, G. P. (2024) · AIMS Mathematics · doi:10.3934/math.2024853 · Cited by 19

AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI

Hiniduma, K., Byna, S., Bez, J. L., & Madduri, R. (2024) · Proceedings of the 36th International Conference on Scientific and Statistical Database Management · doi:10.1145/3676288.3676296 · Cited by 15

LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation

He, X., Ban, Y., Zou, J., Wei, T., Cook, C. B., & He, J. (2025) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2025.findings-acl.361 · Cited by 25

Dólares or Dollars? Unraveling the Bilingual Prowess of Financial LLMs Between Spanish and English

Zhang, X., Xiang, R., Yuan, C., Feng, D., Han, W., Lopez-Lira, A., Liu, X.-Y., Qiu, M., Ananiadou, S., Peng, M., et al. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671554 · Cited by 23

Combining Feature Selection and Classification Using LASSO-Based MCO Classifier for Credit Risk Evaluation

Li, X., Zhang, Z., Li, L., & Pan, H. (2024) · Computational Economics · doi:10.1007/s10614-023-10535-8 · Cited by 12

Natural Symbolic Execution-Based Testing for Big Data Analytics

Wu, Y., Humayun, A., Gulzar, M. A., & Kim, M. (2024) · Proceedings of the ACM on software engineering. · doi:10.1145/3660825 · Cited by 13

Variance of belief information

Chen, X., Zhan, T., Zhou, G., & Deng, Y. (2025) · Applied Mathematical Modelling · doi:10.1016/j.apm.2025.116148 · Cited by 12

Fair Machine Guidance to Enhance Fair Decision Making in Biased People

Yang, M., Arai, H., Yamashita, N., & Baba, Y. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642627 · Cited by 16

D-hacking

Black, E., Gillis, T. B., & Hall, Z. Y. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3658928 · Cited by 11

A comprehensive study of coefficient signs in weighted logistic regression

Zeng, G. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e35040 · Cited by 11

Demystifying Domain-adaptive Post-training for Financial LLMs

Ke, Z., Ming, Y., Nguyen, X.-P., Xiong, C., & Joty, S. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.1579 · Cited by 33

A benchmarking framework and dataset for learning to defer in human-AI decision-making

Alves, J. V., Leitão, D., Jesus, S., Sampaio, M. O. P., Liébana, J., Saleiro, P., Figueiredo, M. A. T., & Bizarro, P. (2025) · Scientific Data · doi:10.1038/s41597-025-04664-y · Cited by 10

Fairquant: Certifying and Quantifying Fairness of Deep Neural Networks

Kim, B. H., Wang, J., & Wang, C. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00016 · Cited by 10

Group Fairness via Group Consensus

Chan, E., Liu, Z., Qiu, R., Zhang, Y., Maciejewski, R., & Tong, H. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3659006 · Cited by 9

Privacy Utility Tradeoff Between PETs: Differential Privacy and Synthetic Data

Razi, Q., Datta, S., Hassija, V., Chalapathi, G., & Sikdar, B. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3479317 · Cited by 9

Time Can Invalidate Algorithmic Recourse

Toni, G. D., Teso, S., Lepri, B., & Passerini, A. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732008 · Cited by 13

An Explainable ADASYN‐Based Focal Loss Approach for Credit Assessment

Shahee, S. A., & Patel, R. (2025) · Journal of Forecasting · doi:10.1002/for.3252 · Cited by 7

EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholders

Luo, L. K., Nakao, Y., Chollet, M., Inakoshi, H., & Stumpf, S. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3710908 · Cited by 7

Evaluating Model Explanations without Ground Truth

Rawal, K., Fu, Z., Delaney, E. D., & Russell, C. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732219 · Cited by 11

Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson’s disease behavioral analysis

Raza, I., Jamal, M. H., Qureshi, R., Shahid, A. K., Vistorte, A. O. R., Samad, M. A., & Ashraf, I. (2024) · Scientific Reports · doi:10.1038/s41598-024-57547-4 · Cited by 7

Towards robust neural networks: Exploring counterfactual causality-based repair

Liu, F., Du, X., Ding, H., & Qian, J. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125082 · Cited by 6

CARMA: A practical framework to generate recommendations for causal algorithmic recourse at scale

Majumdar, A., & Valera, I. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3659003 · Cited by 7

Learning-Based Difficulty Calibration for Enhanced Membership Inference Attacks

Shi, H., Ouyang, T., & Wang, A. (2024) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp60621.2024.00012 · Cited by 7

From shallows to depths: unveiling hybrid synthetic data modeling for enhanced learning with privacy considerations in naturally imbalanced datasets

Bhat, K. A., & Sofi, S. A. (2024) · International Journal of Computers and Applications · doi:10.1080/1206212x.2024.2409989 · Cited by 6

Explainable Deep Learning Approaches to Credit Risk Evaluation

Nalule, D., Edekebon, E., Nagwovuma, M., Jjingo, D., & Marvin, G. (2024) · International Conference on Contemporary Computing · doi:10.1145/3675888.3676052 · Cited by 6

Approximation-guided Fairness Testing through Discriminatory Space Analysis

Zhao, Z., Toda, T., & Kitamura, T. (2024) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3691620.3695481 · Cited by 8

Investigating the Effect of Class Balancing Methods on the Performance of Machine Learning Techniques: Credit Risk Application

Milli, M. E. F., Aras, S., & Kocakoç, İ. D. (2024) · İzmir Yönetim Dergisi · doi:10.56203/iyd.1436742 · Cited by 5

The Role of Initial Acceptance Attitudes Toward AI Decisions in Algorithmic Recourse

Tominaga, T., Yamashita, N., & Kurashima, T. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713573 · Cited by 4

A Multidimensional Approach to Ethical AI Auditing

Teixeira, S., Cortés, À., Thilakarathne, D. J., Gori, G., Minici, M., Bhuyan, M. H., Khairova, N., Adewumi, T., Bhuyan, D., O'Keefe, J., et al. (2025) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1609/aies.v8i3.36732 · Cited by 4

Enhancing Counterfactual Explanation Search with Diffusion Distance and Directional Coherence

Domnich, M., & Vicente, R. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-63800-8_4 · Cited by 4

Constructing Surrogate Models in Machine Learning Using Combinatorial Testing and Active Learning

Shree, S., Khadka, K., Lei, Y., Kacker, R. N., & Kuhn, D. R. (2024) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3691620.3695532 · Cited by 4

Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications

Coelho, D., Madureira, A. M., Pereira, I. F., Gonçalves, R., Nicola, S., César, I., & de Oliveira, D. A. (2025) · Applied Sciences · doi:10.3390/app15158196 · Cited by 3

DiCE-Extended: A Robust Approach to Counterfactual Explanations in Machine Learning

Bakir, V., Göktaş, P., & Akyüz, S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-08384-5_25 · Cited by 3

How Much Can Stratification Improve the Approximation of Shapley Values?

Kolpaczki, P., Haselbeck, G., & Hüllermeier, E. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-63797-1_25 · Cited by 3

A comparative study of pre-processing algorithms for fair classification in few labeled data context

de Oliveira, W. D. G., & Berton, L. (2024) · AI and Ethics · doi:10.1007/s43681-024-00601-8 · Cited by 3

FairWASP: Fast and Optimal Fair Wasserstein Pre-processing

Xiong, Z., Dalmasso, N., Mishler, A., Potluru, V. K., Balch, T. R., & Veloso, M. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i14.29545 · Cited by 3

Nomological Deductive Reasoning for Trustworthy, Human-Readable, and Actionable AI Outputs

Hakizimana, G., & Ledezma Espino, A. (2025) · Algorithms · doi:10.3390/a18060306 · Cited by 2

Bias Mitigation through Proxy Sensitive Attribute Label Generation

Tiwari, D., Pandey, A., & Chaudhari, B. P. (2024) · Joint International Conference on Data Science & Management of Data (CODS-COMAD) · doi:10.1145/3703323.3703329 · Cited by 2

Explanations for Machine Learning Pipelines under Data Drift

Hasan, J., & Pradhan, R. (2025) · Workshop on Human-In-the-Loop Data Analytics (HILDA) · doi:10.1145/3736733.3736744 · Cited by 2

Revisiting Pre-processing Group Fairness: A Modular Benchmarking Framework

Oldfield, B., Xu, Z., & Kandanaarachchi, S. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761650 · Cited by 2

Fairness-Optimized Dynamic Aggregation (FODA): A novel approach to equitable federated learning in heterogeneous environments

Liu, Z., Mazzuchi, T. A., & Sarkani, S. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131807 · Cited by 2

Software Unfairness Detection in Machine Learning-Based Systems: A Systematic Mapping Study

Alharbi, R., & Abbadeni, N. (2026) · Software · doi:10.3390/software5020018 · Cited by 2

Performance Analysis of Comparison Operations on Homomorphically Encrypted Sensitive Data

Naito, H., Matsumoto, M., Nakano, M., & Oguchi, M. (2023) · IEEE International Conference on Consumer Electronics - Berlin (ICCE-Berlin) · doi:10.1109/icce-berlin58801.2023.10375623 · Cited by 2

Mitigating Nonlinear Algorithmic Bias in Binary Classification

Hui, W., & Lau, W. K. (2024) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai59869.2024.00168 · Cited by 2

Identifying Characteristics of Reflection Triggers in Data Science Ethics Education

Linke, D., & Müller-Birn, C. (2024) · Mensch und Computer (MuC) · doi:10.1145/3670653.3677486 · Cited by 2

Enhancing Model Transparency: A Dialogue System Approach to XAI with Domain Knowledge

Feustel, I., Rach, N., Minker, W., & Ultes, S. (2024) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2024.sigdial-1.22 · Cited by 2

Can Causal DAGs Generate Data-based Explanations of Black-box Models?

Ashkari, A., & Rezig, E. K. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825482 · Cited by 2

An empirical study on the application of KANs for classification

Costa, S. S., Pato, M. P. M., & Datia, N. (2024) · International Conference on Advances in Artificial Intelligence (ICAAI) · doi:10.1145/3704137.3704166 · Cited by 2

Fairness Shields: Safeguarding against Biased Decision Makers

Cano, F., Henzinger, T. A., Könighofer, B., Kueffner, K., & Mallik, K. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i15.33719 · Cited by 2

Navigating explanatory multiverse through counterfactual path geometry

Sokol, K., Small, E. A., & Xuan, Y. (2025) · Machine Learning · doi:10.1007/s10994-025-06769-2 · Cited by 2

Enhancing machine learning efficacy and fairness in automated decision systems: an adversarial deep generative modeling with CoBS-TGAN approach in imbalanced and biased datasets

Bhat, K. A., & Sofi, S. A. (2024) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-024-02430-8 · Cited by 2

FIPSER: Improving Fairness Testing of DNN by Seed Prioritization

Chen, J., Zhang, Y., Zhang, L., Zhang, M., Wan, C., Su, T., & Pu, G. (2024) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3691620.3695486 · Cited by 2

Credit Scoring: A Comparison of Machine Learning Models and Their Modifications

Ong, J. C., & Lee, L. S. (2025) · Applied Mathematics and Computational Intelligence (AMCI) · doi:10.58915/amci.v14i1.1362 · Cited by 2

Fairness Testing of Machine Learning Models using Combinatorial Testing in Latent Space

Dahal, A., Shree, S., Lei, Y., Kacker, R. N., & Kuhn, D. R. (2025) · IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) · doi:10.1109/icstw64639.2025.10962484 · Cited by 2

Correct-by-Construction: Certified Individual Fairness through Neural Network Training

Zhang, R.-H., & Sun, J. (2025) · Proc. ACM Program. Lang. · doi:10.1145/3763107 · Cited by 2

Automatic Adjusting Global Similarity Measures in Learning CBR Systems

Ottersen, S. G., & Bach, K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-63646-2_2 · Cited by 1

Repairs and Breaks Prediction for Deep Neural Networks

Ishimoto, Y., Kondo, M., Ma, L., Ubayashi, N., & Kamei, Y. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3702983 · Cited by 1

PICE: Polyhedral Complex Informed Counterfactual Explanations

Villani, M. J., Albini, E., Sharma, S. S. K., Mishra, S., Amoukou, S. I., Magazzeni, D., & Veloso, M. (2024) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1609/aies.v7i1.31742 · Cited by 1

Minimax Group Fairness in Strategic Classification

Diana, E., Sharifi-Malvajerdi, S., & Vakilian, A. (2025) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml64287.2025.00047 · Cited by 1

Perfect counterfactuals in imperfect worlds: modelling noisy implementation of actions in sequential algorithmic recourse

Xuan, Y., Sokol, K., Sanderson, M., & Chan, J. (2025) · Machine Learning · doi:10.1007/s10994-025-06821-1 · Cited by 1

How Much Effort Is Enough? Fairness in Algorithmic Recourse Through the Lens of Substantive Equality of Opportunity

Bell, A. S., Fonseca, J., Abrate, C., Bonchi, F., & Stoyanovich, J. (2025) · ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) · doi:10.1145/3757887.3763014 · Cited by 1

Central moments of belief information

Chen, X., Lin, D., Zhan, T., & Deng, Y. (2025) · Information Sciences · doi:10.1016/j.ins.2025.122951 · Cited by 1

A Systematic Review on Human Roles, Solutions, and Methodological Approaches to Address Bias in AI

Hashky, A., & Ragan, E. D. (2026) · ACM Computing Surveys · doi:10.1145/3793667 · Cited by 3

Analyzing Deviations from Monotonic Trends through Database Repair

Agmon, S., Gal, J., Gilad, A., Livshits, E., Mutay, O., Youngmann, B., & Kimelfeld, B. (2026) · Proceedings of the ACM on Management of Data · doi:10.1145/3786624 · Cited by 1

Robust distance-based generalized linear models: a new tool for classification

del Val, E. B., Grané, A., & Mayo-Íscar, A. (2026) · Statistical Methods & Applications · doi:10.1007/s10260-026-00860-1 · Cited by 1

Feature-weighted maximum representative subsampling

Hauptmann, T., & Krämer, S. (2026) · Scientific Reports · doi:10.1038/s41598-026-54180-1 · Cited by 1

Constructive Specification for Plug-and-Play Learnware Agents

Liu, J.-D., Zhao, Z.-C., Sun, H., Wu, L.-X., Zhang, H., Wang, P., Zhao, M., Chu, X., Yan, S., Zhu, Y., et al. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3817996 · Cited by 1

Strategies Selection for a Fair Classification in Logistic Regression: A Comparative Analysis

Pinheiro, M. V., Silva, M. D. L. M., & Machado, J. C. (2023) · Simpósio Brasileiro de Bancos de Dados (SBBD) · doi:10.5753/sbbd_estendido.2023.232722 · Cited by 1

Automated Fairness Testing with Representative Sampling

Karakas, U., & Tosun, A. (2023) · doi:10.1145/3617555.3617871 · Cited by 1

Optimizing Learning-to-Rank Models for Ex-Post Fair Relevance

Gorantla, S., Bhansali, E., Deshpande, A., & Louis, A. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657751 · Cited by 1

Boundary-Guided Black-Box Fairness Testing

Yin, Z., Zhao, W., & Song, T. (2024) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac61105.2024.00163 · Cited by 2

Estudo experimental sobre justiça algorítmica aplicada em modelos de análise de crédito

de Oliveira, T. A., de Oliveira, J. B., Farias, T. P., Cruz, E. W. R., Andrade, L. J. S., & Pita, R. (2024) · Simpósio Brasileiro de Bancos de Dados (SBBD) · doi:10.5753/sbbd_estendido.2024.243797 · Cited by 1

Aggregations of Fuzzy Equivalences in k-means Algorithm

Lasek, P., Rząsa, W., & Król, A. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.09.502 · Cited by 1

Reject Inference as a Noisy Label Detection and Counterfactual Correction Task

Chelmis, C., Azarshab, M., Rahman, K. S., & Mirpourian, M. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825183 · Cited by 1

A novel financial performance metric to minimize misclassification costs in model selection

Martin, J. K., Abdollahian, M., Taheri, S., & Akman, D. (2025) · Annals of Operations Research · doi:10.1007/s10479-025-06514-x · Cited by 1

BIG: A Practical Framework for Balancing the Conflict Between Group and Individual Fairness in Graph Neural Networks

Yan, K., Matsypura, D., & Gao, J. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00332 · Cited by 1

Proxy Attribute Discovery in Machine Learning Datasets via Inductive Logic Programming

Gonçalves, R. S. D. M., Gouveia, F., Lynce, I., & Santos, J. F. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-90653-4_17 · Cited by 2

LBC: Language-Based-Classifier for Out-Of-Variable Generalization

Noh, K., Seong, B., Byun, H., Choi, Y., Song, S., & Song, K. (2025) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2025.naacl-long.583 · Cited by 1

Hidden Conflicts in Neural Networks and their Implications for Explainability

Dejl, A., Zhang, D., Ayoobi, H., Williams, M., & Toni, F. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732100 · Cited by 4

Multi-Objective Fairness Approach Using Causal Bayesian Networks & Grammatical Evolution

Irfan, Z., Loughran, R., Raja, M. A., & McCaffery, F. (2025) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3712255.3726716 · Cited by 1

Shapley Value-Based Feature Attribution for Data Masking

Qu, X., Darku, F. B., & Guo, H. Y. (2025) · MIS Quarterly · doi:10.25300/misq/2025/18502 · Cited by 1

A Privacy-Preserving Evolutionary Computation Framework for Feature Selection

Sun, B., Li, J.-Y., Liu, X., Yang, Q., Zhan, Z., & Zhang, J. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-7254-8_20 · Cited by 1

SafeAR: Safe Algorithmic Recourse by Risk-Aware Policies

Wu, H., Sharma, S., Patra, S., & Gopalakrishnan, S. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i14.29522 · Cited by 2

Proceedings of the 2023 on Explainable and Safety Bounded, Fidelitous, Machine Learning for Networking

(2023) · doi:10.1145/3630050 · Cited by 1

Statlog (Australian Credit Approval)

Quinlan, R. (1987) · UC Irvine · doi:10.24432/c59012 · Cited by 29

Statlog (Heart)

Unknown (n.d.) · UC Irvine · doi:10.24432/c57303 · Cited by 28

Statlog Project

C. Feng, A. S. (1993) · UC Irvine · doi:10.24432/c5xs3b · Cited by 1

Statlog (Heart) Data Set

Sarmento, R. (2019) · doi:10.13140/rg.2.2.23301.91368

Statistics

Breiman, L. (n.d.) · UCLA - Biblioteca de Administración y Contaduría