Combination of Fuzzy Sets and Rough Sets for Machine Learning Purposes (Tutorial Lecture — Extended Abstract)

• Autorzy: Cornelis Chris , Bollaert Henri

Identyfikatory

DOI

10.15439/2023F0001

• Języki publikacji: EN

Abstrakty

EN

Fuzzy set theory is a popular AI tool designed to model and process vague information. Specifically, it is based on the idea that membership to a given concept, or logical truthhood of a given proposition, can be a matter of degree. On the other hand, rough set theory was proposed as a way to handle potentially inconsistent data inside information systems. In Pawlak's original proposal, this is achieved by providing a lower and upper approximation of a concept, using the equivalence classes of an indiscernibility relation as building blocks. Noting the highly complementary characteristics of fuzzy sets and rough sets, Dubois and Prade proposed the first working definition of a fuzzy rough set, and thus paved the way for a flourishing hybrid theory with numerous theoretical and practical advances. In this tutorial, we will explain how fuzzy rough sets may be successfully applied to a variety of machine learning problems. After a brief discussion of how the hybridization between fuzzy sets and rough sets may be achieved, including an extension based on ordered weighted average operators, we will focus on the following practical applications: 1. Fuzzy-rough nearest neighbor (FRNN) classification, along with its adaptations for imbalanced datasets and multi-label datasets 2. Fuzzy-rough feature selection (FRFS) 3. Fuzzy-rough instance selection (FRIS) and Fuzzy-rough prototype selection (FRPS) We will also demonstrate software implementations of all of these algorithms in the Python library fuzzy-rough-learn.

Słowa kluczowe

EN

computer science; fuzzy sets; rough sets; tutorials; machine learning

PL

informatyka; zbiory rozmyte; zbiory przybliżone; samouczek; uczenie maszynowe

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2023
• Tom: Vol. 35
• Strony: 67--67
• Opis fizyczny: Bibliogr. 22 poz.

Twórcy

autor

Cornelis Chris

• Computational Web Intelligence, Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium

autor

Bollaert Henri

• Computational Web Intelligence, Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium

Bibliografia

• 1. C. Cornelis, M. De Cock, A.M. Radzikowska, Fuzzy rough sets: from theory into practice, in: Handbook of Granular Computing (W. Pedrycz, A. Skowron, V. Kreinovich, eds.), Wiley, 2008, pp. 533–552.
• 2. C. Cornelis, R. Jensen, A noise-tolerant approach to fuzzy-rough feature selection, in: Proc. 2008 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2008), 2008, pp. 1598–1605.
• 3. C. Cornelis, R. Jensen, G. Hurtado Martín, and D. Ślęzak, Feature selection with fuzzy decision reducts, in: Proc. Third International Conference on Rough Sets and Knowledge Technology (RSKT 2008), 2008, pp. 284–291.
• 4. C. Cornelis, N. Verbiest, and R. Jensen, Ordered weighted average based fuzzy rough sets, in: Proc. 5th International Conference on Rough Sets and Knowledge Technology (RSKT 2010), 2010, pp. 78–85.
• 5. M. De Cock, C. Cornelis, E.E. Kerre, Fuzzy rough sets: beyond the obvious, in: Proc. 2004 IEEE Int. Conf. on Fuzzy Systems, FUZZ-IEEE’04, Volume 1, 2004, pp. 103-108.
• 6. L. D’eer, N. Verbiest, C. Cornelis, L. Godo, A comprehensive study of implicator-conjunctor based and noise-tolerant fuzzy rough sets: definitions, properties and robustness analysis, Fuzzy Sets and Systems 275, 2015, pp. 1–38.
• 7. D. Dubois and H. Prade, Rough fuzzy sets and fuzzy rough sets, International Journal of General Systems 17, 1990, pp. 91–209.
• 8. M. Inuiguchi, W.Z. Wu, C. Cornelis, N. Verbiest, Fuzzy-rough hybridization, in: Springer Handbook of Computational Intelligence, 2015, pp. 425–451.
• 9. R. Jensen, C. Cornelis, Fuzzy-rough instance selection, in: Proc. 19th International Conference on Fuzzy Systems (FUZZ-IEEE 2010), 2010, pp. 1776–1782.
• 10. R. Jensen and C. Cornelis, Fuzzy-rough nearest neighbour classification, Transactions on rough sets, vol. XIII, 2011, pp. 56–72.
• 11. O.U. Lenz, D. Peralta, C. Cornelis, Scalable approximate FRNN-OWA classification, IEEE Transactions on Fuzzy Systems 28(5), 2020, pp. 929–938.
• 12. O.U. Lenz, D. Peralta, C. Cornelis, Fuzzy-rough-learn 0.1: A Python library for machine learning with fuzzy rough sets, in: Proc. International Joint Conference on Rough Sets, 2020, pp. 491–499.
• 13. O.U. Lenz, D. Peralta, C. Cornelis, Fuzzy-rough-learn 0.2: a Python library for fuzzy rough set algorithms and one-class classification, in: Proc. 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2022, pp. 1–8.
• 14. Z. Pawlak, Rough sets, International Journal of Computer and Information Sciences 11(5), 1982, pp. 341–356.
• 15. E. Ramentol, S. Vluymans, N. Verbiest, Y. Caballero, R. Bello, C. Cornelis, F. Herrera, IFROWANN: imbalanced fuzzy-rough ordered weighted average nearest neighbor classification, IEEE Transactions on Fuzzy Systems 23(5), 2015, pp. 1622–1637.
• 16. N. Verbiest, C. Cornelis, F. Herrera, FRPS: a fuzzy rough prototype selection method, Pattern Recognition 46(10), 2013, pp. 2770–2782.
• 17. N. Verbiest, C. Cornelis, F. Herrera, OWA-FRPS: a prototype selection method based on ordered weighted average fuzzy rough set theory, in: Proc. 14th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing ( RSFDGrC 2013), 2013, pp. 180–190.
• 18. S. Vluymans, L. D’eer, Y. Saeys, C. Cornelis, Applications of fuzzy rough set theory in machine learning: a survey, Fundamenta Informaticae 142(1-4), 2015, pp. 53–86.
• 19. S. Vluymans, A. Fernández, Y. Saeys, C. Cornelis, F. Herrera, Dynamic affinity-based classification of multi-class imbalanced data with one-vs-one decomposition: a fuzzy rough approach, Knowledge and Information Systems 6(1), 2018, pp. 55–84.
• 20. S. Vluymans, C. Cornelis, F. Herrera, Y. Saeys, Multi-label classification using a fuzzy rough neighborhood consensus, Information Sciences 433-434, 2018, pp. 96–114.
• 21. S. Vluymans, N. Mac Parthalain, C. Cornelis, Y. Saeys, Weight selection strategies for ordered weighted average based fuzzy rough sets, Information Sciences 501, 2019, pp. 155–171.
• 22. L.A. Zadeh, Fuzzy sets, Information and Control 8, 1965, pp. 338–353.

Uwagi

1. Main Track Invited Contributions

2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).