A Novel Method for Improving Deep Learning Performance Using Rough Set Theory
By: Piotr Pięta, Tomasz Szmuc and Rafał Mrówka
References
- Das A. K., Sengupta S., and Bhattacharyya S. A group incremental feature selection for classification using rough set theory based genetic algorithm. Applied Soft Computing, 65:400–411, 2018.
- S.O Arik and T. Pfister. TabNet: Attentive Interpretable Tabular Learning, 2020.
- J. Bazan, M. Szczuka, and J. Wróblewski. A New Version of Rough Set Exploration System. In Lecture Notes in Artificial Intelligence, volume 2475 of Lecture Notes in Computer Science, pages 397– 404. Springer-Verlag, Berlin, Heidelberg, 2002.
- J.G. Bazan, S. Bazan-Socha, U. Bentkowska, W. Gałka, M. Mrukowicz, and L. Zaręba. Comparison of aggregation classes in ensemble classifiers for high dimensional datasets. In 2022 IEEE International Conference on Fuzzy Systems (FUZZIEEE), pages 1–10, 2022.
- J. Bilski, B. Kowalczyk, L. Dymova, and M. Xiao. Accelerating Neural Network Training with FSGQR: A Scalable and High-Performance Alternative to Adam. Journal of Artificial Intelligence and Soft Computing Research, 15(2):95–113, 2025.
- Ch.M Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
- V. Borisov, T. Leemann, K. Seßler, J. Haug, M. Pawelczyk, and G. Kasneci. Deep Neural Networks and Tabular Data: A Survey. IEEE Transactions on Neural Networks and Learning Systems, 35(6):7499–7519, 2024.
- J. Błaszczy´nski, S. Greco, B. Matarazzo, R. Słowin ´ski, and M. Szela˛g. jMAF - Dominance-Based Rough Set data Analysis Framework. In Intelligent Systems Reference Library, volume 42, pages 185–209. Springer, 2013.
- F. Chollet. Keras. https://keras.io, 2015.
- S. Dutta and A. Skowron. Information System in the Light of Interactive Granular Computing. In Mengjun Hu, Chris Cornelis, Yan Zhang, Pawan Lingras, Dominik Ślęzak, and JingTao Yao, editors, Rough Sets, pages 223–237, Cham, 2024. Springer Nature Switzerland.
- W.H. Elashmawi, A. Sheta, B.S. Alqadi, D.S. AbdElminaam, and D.M. Alsekait. A New Version of the Golden Eagle Optimizer Algorithm And Its Application For Solving A Trio-Objective Skillful Team Formation Problem In A Social Network. Journal of Artificial Intelligence and Soft Computing Research, 15(4):357–384, 2025.
- A.E. Ezugwu, A.M. Ikotun, O. O. Oyelade, L. Abualigah, J. O. Agushaka, Ch. I. Eke, and A. A. Akinyelu. A Comprehensive Survey of Clustering Algorithms: State-of-the-Art Machine Learning Applications, Taxonomy, Challenges, and Future Research Prospects. Engineering Applications of Artificial Intelligence, 110:104743, 2022.
- B. Ganter and R. Wille. Formal Concept Analysis: Mathematical Foundations. Springer, 1999.
- M. Garbulowski, K. Diamanti, K. Smoli´nska, et al. R.ROSETTA: an interpretable machine learning framework. BMC Bioinformatics, 22(1):110, 2021.
- W. Gałka, J. Bazan, U. Bentkowska, K. Szwed, M. Mrukowicz, P. Dryga´s, L. Zaręba, M. Szpyrka, P. Suszalski, and S. Obara. Aggregation-Based Ensemble Classifier Versus Neural Networks Models for Recognizing Phishing Attacks. IEEE Access, 13:48469–48487, 2025.
- I. Goodfellow, Y. Bengio, and A. Courville. Deep Learning. MIT Press, 2016.
- Y. Gorishniy, I. Rubachev, V. Khrulkov, and A. Babenko. Revisiting deep learning models for tabular data. In Proceedings of the 35th International Conference on Neural Information Processing Systems, NIPS ’21, Red Hook, NY, USA, 2021. Curran Associates Inc.
- M. Grzegorowski, A. Janusz, Ł. Marcinowski, A. Skowron, D. Ślęzak, and G. ´Sliwa. On Explainability of Cluster Prototypes with Rough sets: A Case Study in the FMCG Market. International Journal of Applied Mathematics and Computer Science, 35(1):19–31, 2025.
- J.W. Grzymala-Busse. LERS-A data mining system. In O. Maimon and L. Rokach, editors, Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA, 2005.
- G. Góra and A. Skowron. RIONIDA: A novel algorithm for imbalanced data combining instancebased learning and rule induction. Information Sciences, 708:122015, 2025.
- Sakai H., Nakata M., Ślęzak D., and Watada J. Rule generation in Rough set Non-deterministic Information analysis (RNIA) and some applications of the obtained rules. Applied Soft Computing, 172:112842, 2025.
- T. Hachaj and J. Wa˛s. An insightful data-driven crowd simulation model based on rough sets. Information Sciences, 692:121670, 2025.
- T. Hachaj and J. Wa˛s. Rough neighborhood graph: A method for proximity modeling and data clustering. Applied Soft Computing, 171:112789, 03 2025.
- A.A. Hagberg, D.A. Schult, and P.J. Swart. Exploring Network Structure, Dynamics, and Function using NetworkX. Python in Science Conference, 2008.
- J. Han, J. Pei, and M. Kamber. Data Mining: Concepts and Techniques. Elsevier, 4 edition, 2021.
- R.C. Holte. Very Simple Classification Rules Perform Well on Most Commonly Used Datasets. Machine Learning, 11:63–90, 1993.
- Bazan J. and Szczuka M. The Rough Set Exploration System. Transactions on Rough Sets III, 3400:37–56, 2005.
- Jing J. Research on decision-making evaluation system based on rough set theory. In Proceedings of the 2023 8th International Conference on Intelligent Information Processing, ICIIP ’23, page 174–178, New York, NY, USA, 2023. Association for Computing Machinery.
- Bazan J. G. A Comparison of Dynamic and Non-Dynamic Rough Set Methods for Extracting Laws from Decision Tables. In L. Polkowski and A. Skowron, editors, Rough Sets in Knowledge Discovery 1: Methodology and Applications, pages 321–365. Physica-Verlag, Heidelberg, 1998.
- G. James, D. Witten, T. Hastie, and R. Tibshirani. An Introduction to Statistical Learning: with Applications in R. Springer, 2 edition, 2021.
- A. Janusz, D. Ślęzak, S. Stawicki, and K. Stencel. A practical study of methods for deriving insightful attribute importance rankings using decision bireducts. Information Sciences, 645:119354, 2023.
- R. Jensen. Fuzzy-Rough Data Mining. In Rough Sets and Current Trends in Computing (RSCTC 2010), pages 31–35, Berlin, Heidelberg, 2011. Springer.
- R. Jensen and Q. Shen. Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy- Rough-Based Approaches. IEEE Transactions on Knowledge and Data Engineering, 16(12):1457– 1471, 2004.
- D.S. Johnson. Approximation Algorithms for Combinatorial Problems. Journal of Computer and System Sciences, 9(3):256–278, 1974.
- I.T. Jolliffe and J. Cadima. Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 2016.
- J. Jomal, M. Bibin, J.J. Sunil, and Jobish V. Integrating Rough Sets and Multidimensional Fuzzy Sets for Approximation Techniques: A Novel Approach. IEEE Access, 12:154796–154810, 2024.
- J. Komorowski, A. Øhrn, and A. Skowron. The ROSETTA Rough Set Software System. In W. Klösgen and J. Zytkow, editors, Handbook of Data Mining and Knowledge Discovery, chapter D.2.3. Oxford University Press, 2002.
- V. Latora, V. Nicosia, and G. Russo. Complex Networks: Principles, Methods and Applications. Cambridge University Press, 2017.
- Z. Liu, M. Zhao, Sh. Zhao, J. Luo, and F. Luo. Opinion Evolution and Guidance Model Based on Social Networks and Information Networks. Journal of Artificial Intelligence and Soft Computing Research, 16(2):105–123, 2026.
- Z. Lv, H. Song, P. Basanta-Val, A. Steed, and M. Jo. Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics. IEEE Transactions on Industrial Informatics, 13(4):1891–1899, 2017.
- E. Masciari, A. Umair, and M.H. Ullah. A systematic Literature Review on AI-Based Recommendation Systems and Their Ethical Considerations. IEEE Access, 12:121223–121241, 2024.
- S. Mitra, S.K. Pal, and P. Mitra. Data mining in soft computing framework: a survey. IEEE Transactions on Neural Networks, 13(1):3–14, 2002.
- M. Ju. Moshkov, M. Piliszczuk, and B. Zielosko. Partial Covers, Reducts and Decision Rules in Rough Sets: Theory and Applications. Springer Publishing Company, Incorporated, 1st edition, 2009.
- S. Naouali and O. El Othmani. Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models. Applied Sciences, 15(9):5148, 2025.
- M. Newman. Networks. Oxford University Press, 2nd edition, 07 2018.
- H.S. Nguyen and A. Skowron. Quantization of Real-Valued Attributes in Decision Tables – A Rough Set Approach. International Journal of Approximate Reasoning, 13(2):119–148, 1995.
- R.K. Nowicki, R. Seliga, D. ˙ Zelasko, and Y. Hayashi. Performance analysis of rough set–based hybrid classification systems in the case of missing values. Journal of Artificial Intelligence and Soft Computing Research, 11(4):307– 318, 2021.
- P. Okeleke, D. Ajiga, S. Folorunsho, and Ch. Ezeigweneme. Predictive Analytics for Market Trends using AI: A Study in Consumer Behavior. International Journal of Engineering Research Updates, 7(1), August 2024.
- R. Olszowski, M. Zabdyr-Jamróz, S. Baran, P. Pięta, and W. Ahmed. A Social Network Analysis of Tweets Related to Mandatory COVID-19 Vaccination in Poland. Vaccines (Basel), 10(5):750, May 2022.
- S.K Pal, D. Bhoumik, and D. Bhunia Chakraborty. Granulated deep learning and z-numbers in motion detection and object recognition. Neural Computing and Applications, 32(7):16533–16548, 2020.
- A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al. Pytorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information Processing Systems, 32, 2019.
- Z. Pawlak. Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Norwell, USA, 1991.
- Z. Pawlak and A. Skowron. Rudiments of rough sets. Information Sciences, 177(1):3–27, 2007. Z. Pawlak life and work (1926–2006).
- Z. Pei, Z. Meng, T. Diao, P. Miao, Y. Meng, and Ch. Tan. A Visually Explainable Dynamic Similarity Network for Few-Shot Classification. Journal of Artificial Intelligence and Soft Computing Research, 16(3):237–256, 2026.
- J.F. Peters, A. Skowron, and J. Stepaniuk. Rough Sets: Foundations and Perspectives, pages 877– 889. Springer US, New York, NY, 2023.
- P. Pięta, T. Szmuc, and K. Kluza. Comparative overview of rough set toolkit systems for data analysis. MATEC Web of Conferences, 252:03019, 2019. III International Conference of Computational Methods in Engineering Science (CMES’18), Kazimierz Dolny, Poland, Nov 22–24, 2018.
- M. Przybyła-Kasperek and K. Kusztal. Rules’ Quality Generated by the Classification Method for Independent Data Sources Using Pawlak Conflict Analysis Model. In Jiˇrí Mikyška, Clélia de Mulatier, Maciej Paszynski, Valeria V. Krzhizhanovskaya, Jack J. Dongarra, and Peter M.A. Sloot, editors, Computational Science – ICCS 2023, pages 390–405, Cham, 2023. Springer Nature Switzerland.
- M. Przybyła-Kasperek and K. Opoku. Decision rules for dispersed data using a federated learning approach. Procedia Computer Science, 225:4305– 4313, 2023. 27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems (KES 2023).
- B. Prędki, R. Słowi´nski, J. Stefanowski, R. Susmaga, and S. Wilk. ROSE - software implementation of the rough set theory. In Proceedings of the 2nd Joint European Conference on the Theory and Practice of Software (ETAPS’98), Lecture Notes in Computer Science, pages 605–608, Berlin, Heidelberg, 1998. Springer.
- Y. Qian, X. Liang, Q. Wang, J. Liang, B. Liu, A. Skowron, Y. Yao, J. Ma, and Ch. Dang. Local rough set: A solution to rough data analysis in big data. International Journal of Approximate Reasoning, 97:38–63, 2018.
- Z. Qiao, A.A. Heidari, X. Zhao, and H. Chen. An Evolutionary Neural Architecture search method Accelerated by Multi-Fidelity Evaluation and Genetic Decision Controller. Journal of Artificial Intelligence and Soft Computing Research, 15(4):413–446, 2025.
- L.S. Riza, A. Janusz, Ch. Bergmeir, Ch. Cornelis, F. Herrera, D. Ślęzak, and J.M. Benítez. Implementing algorithms of rough set theory and fuzzy rough set theory in the R package RoughSets. Information Sciences, 287:68–89, 2014.
- H. Sakai, M. Nakata, D. Ślęzak, and J. Watada. Consideration of Detecting Data and Functional Dependency in Tabular Data with Missing Values by the Obtained Rules. In M. Hu, Ch. Cornelis, Y. Zhang, P. Lingras, D. Ślęzak, and J. Yao, editors, Rough Sets, pages 120–133, Cham, 2024. Springer Nature Switzerland.
- R. Saravanan and Pothula Sujatha. A State of Art Techniques on Machine Learning Algorithms: A Perspective of Supervised Learning Approaches in Data Classification. In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), pages 945–949, 2018.
- S. Sarker, P. Sarker, Stone. G., R. Gorman, A. Tavakkoli, G. Bebis, and J. Sattarvand. A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation. Machine Vision and Applications, 35(4):67, 2024.
- D. Saumendra and J. Nayak. Customer Segmentation via Data Mining Techniques: State-of-the-Art Review, pages 489–507. Springer, Singapore, 01 2022.
- J. Sawicki, M. Ganzha, and M. Paprzycki. The State of the Art of Natural Language Processing – A Systematic Automated Review of NLP Literature Using NLP Techniques. Data Intelligence, 5:1–47, 07 2023.
- R. Sen, A.K. Mandal, and B. Chakraborty. A Critical Study on Stability Measures of Feature Selection with a Novel Extension of Lustgarten Index. Machine Learning and Knowledge Extraction, 3(4):771–787, 2021.
- N. Shakhovska, A. Shebeko, and Y. Prykarpatskyy. A Novel Explainable AI Model for Medical Data Analysis. Journal of Artificial Intelligence and Soft Computing Research, 14(2):121–137, 2024.
- Q. Shen and R. Jensen. Selecting Informative Features with Fuzzy-Rough Sets and Its Application for Complex Systems Monitoring. Pattern Recognition, 37(7):1351–1363, 2004.
- W. Siedlecki and J. Sklansky. A note on genetic algorithms for large-scale feature selection. Pattern Recognition Letters, 10(5):335–347, 1989.
- A. Skowron. Informational Granules in Interactive Granular Computing. Computer Sciences & Mathematics Forum, 8(1), 2023.
- A. Skowron, A. Jankowski, and S. Dutta. Interactive granular computing. Granular Computing, 1(2):95–113, 2016.
- A. Skowron and C. Rauszer. The Discernibility Matrices and Functions in Information Systems, pages 331–362. Springer Netherlands, Dordrecht, 1992.
- A. Skowron and J. Stepaniuk. Toward rough set based insightful reasoning in intelligent systems. Information Sciences, 709:122078, 2025.
- A. Skowron and D. Ślęzak. Rough sets Turn 40: From Information Systems to Intelligent Systems. In 2022 17th Conference on Computer Science and Intelligence Systems (FedCSIS), pages 23–34, 2022.
- V. S. Subha and R. Selvakumar. An Innovative Electric Vehicle Selection with Multi-Criteria Decision-making Approach in Indian brands on a Neutrosophic Hypersoft Rough Set by using Reduct and Core. In 2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), pages 1–7, Jan 2024.
- M. Suzuki. FT-Transformer: Transformer for Tabular Data. https: www.kaggle.com/code/masatakasuzuki/ft-transformer-transformer-for-tabular-data , 2023. Kaggle Notebook, accessed 2026-02-01.
- A. Szczur, J.G. Bazan, U. Bentkowska, P. Kruczek, and S. Bazan-Socha. Identifying and Predicting Changes in Behavioral Patterns for Temporal Data in Treatment of Neonatal Respiratory Failure. Applied Sciences, 15(22), 2025.
- J. Tan, Q. Hou, X. Liu, and Y. Xiong. Research on energy consumption prediction based on fast attribute reduction of weighted neighborhood rough set with moving horizon. In Proceedings of the 2023 7th International Conference on Machine Learning and Soft Computing, ICMLSC ’23, page 18–25, New York, NY, USA, 2023. Association for Computing Machinery.
- Sanjeev Verma, Rohit Sharma, Subhamay Deb, and Debojit Maitra. Artificial Intelligence in Marketing: Systematic Review and Future Research Direction. International Journal of Information Management Data Insights, 1(1):100002, 2021.
- D. Vetrithangam, Naresh Kumar Pegada, R. Himabindu, and A. Ramesh Kumar. A State of Art Review on Image Analysis Techniques, Datasets, and Applications. AIP Conference Proceedings, 3072(1):040002, 03 2024.
- Q. Wang, Y. Qian, X. Liang, Q. Guo, and J. Liang. Local neighborhood rough set. Knowledge-Based Systems, 153:53–64, 2018.
- S. Wasserman and K. Faust. Social Network Analysis: Methods and Applications. Structural Analysis in the Social Sciences. Cambridge University Press, 1994.
- S. Xia, C. Wang, G. Wang, X. Gao, W. Ding, J. Yu, Y. Zhai, and Z. Chen. GBRS: A Unified Granular-Ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set. IEEE Transactions on Neural Networks and Learning Systems, 36(1):1719–1733, Jan 2025.
- Y. Yao. A Comparative Study of Formal Concept Analysis and Rough Set Theory in Data Analysis. In Shusaku Tsumoto, Roman Słowiński, Jan Komorowski, and Jerzy W. Grzymała-Busse, editors, Rough Sets and Current Trends in Computing, pages 59–68, Berlin, Heidelberg, 2004. Springer Berlin Heidelberg.
- Y. Y. Yao and Yiyu Zhao. Discernibility matrix simplification for constructing attribute reducts. Information Sciences, 179(5):867–882, 2009.
- K. Yuan, W. Xu, and D. Miao. A Local Rough Set method for feature selection by Variable Precision composite measure. Applied Soft Computing, 155:111450, 2024.
- Q. Zhang, Q. Xie, and G.Wang. A survey on rough set theory and its applications. CAAI Transactions on Intelligence Technology, 1(4):323–333, 2016.
- W. Zhang and Z. Yang. Methods and Applications of Data Management and Analytics. Applied Sciences, 14(24), 2024.
- W. Ziarko. Variable Precision Rough Set Model. Journal of Computer and System Sciences, 46(1):39–59, 1993.
- R. Świniarski and A. Skowron. Rough set method in feature selection and recognition. Pattern Recogn. Lett. 24, 833-849. Pattern Recognition Letters, 24:833–849, 03 2003.
Language: English
Page range: 61 - 84
Submitted on: Mar 13, 2026
Accepted on: May 19, 2026
Published on: Jul 1, 2026
Published by: SAN University
In partnership with: Paradigm Publishing Services
Keywords:
Related subjects:
© 2026 Piotr Pięta, Tomasz Szmuc, Rafał Mrówka, published by SAN University
This work is licensed under the Creative Commons Attribution 4.0 License.