Uncertainty-Aware Feature Selection Framework Based on Three-Way Decision Theory for Explainable Machine Learning
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V7I2P145Keywords:
Three-Way Decision Theory, Uncertainty-Aware Feature Selection, Explainable Machine Learning, Feature Relevance, Feature Uncertainty, Rough Set Theory, Model Interpretability, Decision-Theoretic LearningAbstract
Feature selection is a critical stage in machine learning because it improves predictive performance, reduces computational complexity, and enhances model interpretability. However, many existing feature selection methods rely on binary selection strategies that either retain or discard features based on fixed relevance thresholds. This approach can be inadequate when features contain uncertainty, instability, redundancy, or borderline predictive value. To address this limitation, this article proposes an uncertainty-aware feature selection framework based on three-way decision theory for explainable machine learning. The proposed framework classifies features into three decision regions: accepted features, rejected features, and deferred features. Accepted features are considered highly relevant and reliable, rejected features are removed due to low relevance or high redundancy, while deferred features are subjected to further evaluation because of uncertain or inconsistent importance. By integrating uncertainty measurement with feature relevance scoring, the framework provides a more flexible and transparent alternative to conventional two-way feature selection. The framework also supports explainability by providing clear justification for why each feature is selected, rejected, or deferred. This makes it particularly useful for high-dimensional and high-stakes machine learning applications where model transparency, reliability, and decision accountability are important. The proposed approach is expected to improve feature selection stability, reduce uncertainty in selected feature subsets, and enhance the interpretability of machine learning models.
References
1. Alsakarnah, R., Alauthman, M., & Almomani, A. (2026). Hybridizing explainable AI for intelligent feature selection in phishing website detection. Electronics, 15(2), 350.
2. Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable artificial intelligence: Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
3. Becker, A. J., & Bagrow, J. P. (2019). UAFS: Uncertainty-aware feature selection for problems with missing data. arXiv. https://doi.org/10.48550/arXiv.1904.01385
4. Campagner, A., Cabitza, F., & Ciucci, D. (2020). Three-way decision for handling uncertainty in machine learning: A narrative review. In Rough Sets: International Joint Conference, IJCRS 2020 (pp. 137–152). Springer. https://doi.org/10.1007/978-3-030-52705-1_10
5. Campagner, A., Milella, F., Ciucci, D., & Cabitza, F. (2024). Three-way decision in machine learning tasks: A systematic review. Artificial Intelligence Review, 57, 228. https://doi.org/10.1007/s10462-024-10845-9
6. Ding, J., Liu, D., & Zhang, X. (2024). Three-way decisions in generalized intuitionistic fuzzy environments: Survey and challenges. Artificial Intelligence Review, 57, 34.
7. Gunning, D., & Aha, D. W. (2019). DARPA’s explainable artificial intelligence program. AI Magazine, 40(2), 44–58. https://doi.org/10.1609/aimag.v40i2.2850
8. Hüllermeier, E., & Waegeman, W. (2021). Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning, 110, 457–506. https://doi.org/10.1007/s10994-021-05946-3
9. Jiménez, S., Jürgens, M., & Waegeman, W. (2025). Why machine learning models fail to fully capture epistemic uncertainty. arXiv. https://doi.org/10.48550/arXiv.2505.23506
10. Liu, K., Xu, W., Wang, G., & Qian, Y. (2026). A survey on rough feature selection: Recent advances and future trends. IEEE/CAA Journal of Automatica Sinica.
11. Luo, J., Zhang, X., & Wang, Y. (2025). Decision-theoretic rough sets for three-way decisions in dilemma reasoning and conflict resolution. Mathematics, 13(13), 2111.
12. Marcinkevičs, R., & Vogt, J. E. (2020). Interpretability and explainability: A machine learning zoo mini-tour. arXiv. https://doi.org/10.48550/arXiv.2012.01805
13. Mehdiyev, N., Majlatow, M., & Fettke, P. (2023). Interpretable and explainable machine learning methods for predictive process monitoring: A systematic literature review. arXiv. https://doi.org/10.48550/arXiv.2312.17584
14. Molnar, C. (2022). Interpretable machine learning: A guide for making black box models explainable (2nd ed.). Leanpub.
15. Mumuni, F., & Mumuni, A. (2025). Explainable artificial intelligence: From inherent explainability to large language models. arXiv. https://doi.org/10.48550/arXiv.2501.09967
16. Rudin, C. (2019). Stop explaining black box machine learning models for high-stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1, 206–215. https://doi.org/10.1038/s42256-019-0048-x
17. Sahraei, M. A., Mehr, A. D., & Yaseen, Z. M. (2025). A multi-stage feature selection and explainable machine learning framework for predictive modeling. Energies, 18(15), 4184.
18. Sale, Y., Hofman, P., Wimmer, L., Bischl, B., & Hüllermeier, E. (2024). Label-wise aleatoric and epistemic uncertainty quantification. Machine Learning.
19. Siddique Ayon, S., Islam, M. M., & Uddin, M. N. (2024). Explainable AI in feature selection: Improving classification performance on imbalanced datasets. In Proceedings of Recent Advances in Artificial Intelligence and Data Science. Springer.
20. Vilone, G., & Longo, L. (2020). Explainable artificial intelligence: A systematic review. arXiv. https://doi.org/10.48550/arXiv.2006.00093
21. Wimmer, L., Sale, Y., Hofman, P., Bischl, B., & Hüllermeier, E. (2023). Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures? In Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence.
22. Yapicioglu, F. R., & Kose, U. (2025). Uncertainty considerations of explainable AI in data-driven decision-making systems. CEUR Workshop Proceedings.
23. Nagraj, A. (2022). Modernizing Legacy Banking Systems: Migration Strategies and Cost Optimization in Financial Enterprises. Frontiers in Computer Science and Artificial Intelligence, 1(1), 43-52.
24. Zacharias, J., von Zahn, M., Chen, J., & Hinz, O. (2022). Designing a feature selection method based on explainable artificial intelligence. Electronic Markets, 32, 2159–2184. https://doi.org/10.1007/s12525-022-00608-1
25. Zhang, C., Gao, R., Qin, H., & Feng, R. (2021). Three-way clustering method for incomplete information system based on set-pair analysis. Granular Computing, 6, 389–398.
26. MARASANI, Y. (2024). Enterprise Readiness for Generative AI: The Critical Role of Data Engineering. Frontiers in Computer Science and Artificial Intelligence, 3(2), 59-71.
27. Zschech, P., Heinrich, K., & Pfitzner, B. (2025). A contrasting paradigm to post-hoc explainable AI: Inherently interpretable machine learning for trustworthy decision support. Business & Information Systems Engineering.
28. Takon, A. (2024). Data-Driven Threat Intelligence for Energy and Critical Asset Management. International Journal of Technology, Management and Humanities, 10(04), 253-266.
29. Kola, J. N. Longitudinal Cohort Intelligence for Self-Insured Employer Groups: A Predictive Framework for Healthcare Cost Trajectory Modeling and Proactive Risk Intervention.
30. Adepoju, S. A., & Adepoju, M. A. (2024). From Portals to Case Graphs: A Reference Architecture and Benchmark for Safety Investigation Operations with Agentic Orchestration.
31. Takon, A. (2024). Data Science Approaches to Asset Integrity Management in Offshore and Onshore Oil and Gas Operations. Multidisciplinary Innovations & Research Analysis, 5(2), 17-31.
32. Kola, J. N. (2011). An Integrated Framework for Data Mining and Distributed Database Optimization in Resource-Constrained Network Environments. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 2(02), 82-86.
33. Ravikumar, V. (2014). Fair and optimal resource allocation in wireless sensor networks.
34. Naidu, K. J. (2014). Secure OLAP Reporting Architectures: Integrating Role-based Access Control and Query Execution Plan Optimization for Enterprise Analytical Environments. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 5(02), 155-159.
35. Karmaker, A., Juthi, Z. T., & Ahmed, S. (2025). Mechanically Responsive Hydrogels as Adhesives for Clinical Applications. Progress in Adhesion and Adhesives, 9, 493-519.
36. Mukherjee, C. Ai-Driven Personalization of Power System Learning Modules Using Student Personas based on Behavioral Analysis of Grid Performance.
37. Nagraj, A. (2024). GraphQL in Wealth Management Platforms: Optimizing Data Access and Performance. British Journal of Multidisciplinary Studies, 2(1), 16-24.
38. Nadia, N. Y., Rabby, H. R., Arif, M. H., Tanvir, M. I. M., Ahmed, M., & Firdaus, S. (2025, October). Scalable RNN-Based Transfer Learning for Patient Sentiment Monitoring in Telehealth Platforms. In 2025 IEEE 2nd International Conference on Computing, Applications and Systems (COMPAS) (pp. 1-6). IEEE.
39. MARASANI, Y. (2023). Machine Learning Models for Predicting Patient Treatment Switching Using Claims Data. Frontiers in Computer Science and Artificial Intelligence, 2(1), 59-66.
40. Takon, A. (2025). Explainable AI for Threat Modelling and Decision Support in Engineering Assets. Journal of Cyber-Physical Security and Robotics, 1(02), 46-52.
41. Mukherjee, C. (2025). Combating digital media piracy with agentic ai: Leveraging video transcription and character recognition for automated enforcement. Authorea Preprints.
42. Anifowose, K. (2025). Development and Validation of AI-Assisted Analytical Methods for Biochemical Compound Detection in Pharmaceutical Chemistry. Journal of Applied Pharmaceutical Sciences and Research, 8(4), 41-52.
43. Karmaker, A., Hasan, M., & Nahid, A. (2021). Design and Development of an Open-Source and Affordable LoT System for Developing and Least Developed Countries.
44. ALAMPALLY, J. (2024). Enhancing data quality and trust in AI systems through robust data engineering. Frontiers in Computer Science and Artificial Intelligence, 3(1), 120-130.
45. Mukherjee, C. (2025). Use of Agentic AI with OpenAI and Prompt Engineering and State-of-the Art Machine Learning Algorithm to detect the patterns in IOT Device Network Intrusion Attacks. Authorea Preprints.
46. Ravikumar, V. (2025). Therapeutic Bot: Ethical Concerns in AI therapy for Neurodivergence. J Int Scient Re Rep.
47. Mukherjee, C. (2025). Use of Agentic AI with LLM and Prompt Engineering and State-of-the Art Machine Learning Algorithm to detect the patterns in IOT Device Network Intrusion Attacks. TechRxiv. August, 6.
48. Takon, A. (2025). 3D Object Detection and Localization for Industrial Threat Monitoring. Well Testing Journal, 34(S3), 850-880.
49. ALAMPALLY, J. (2024). Real-Time and Near-Real-Time Analytics in Healthcare Data Ecosystems. Journal of Computer Science and Technology Studies, 6(1), 314-324.
50. Mukherjee, C. (2025). Harnessing large language models and ai agents for child behavior analytics in day care: a proof of concept for next-generation parental insight using simulated data. Machinery and Production Engineering, 174(2870), 26-34.
51. Mukherjee, C. (2025). Combating digital media piracy with agentic ai: Leveraging video transcription and character recognition for automated enforcement. Authorea Preprints.
52. Takon, A. (2026). AI-Augmented Visual Inspections in Mining and Heavy Industry. Journal of Science Technology and Social Transformation, 2(01), 8-16.
53. Kola, J. N. Privacy-Preserving Federated Analytics Across Multi-Employer Data Ecosystems: A Cross-Organizational Intelligence Architecture for Benchmark-Driven Decision Support in Enterprise HR and Benefits Platforms.
54. Takon, A. (2026). Zero-Shot and Few-Shot Object Detection for Emerging Threat Scenarios. Well Testing Journal, 35(S2), 199-224.
55. Anifowose, K. (2026). Advanced Chromatographic and Spectroscopic Method Development for Biomarker Identification and Validation in Clinical Biochemistry. Journal of Drug Discovery and Health Sciences, 3(02), 1-8.
56. Marasani, Y. (2025). Explainable AI Frameworks for Patient-Level Claims Data Analytics. J Artif Intell Mach Learn & Data Sci, 8(1), 3382-3390.
