Multi-class granular approximation by means of disjoint and adjacent fuzzy granules
[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ 2 ] Instytut Badań Systemowych Polskiej Akademii Nauk | [ P ] pracownik
2024
artykuł naukowy
angielski
- Granular computing
- Fuzzy sets
- Machine learning
EN In granular computing, fuzzy sets can be approximated by granularly representable sets that are as close as possible to the original fuzzy set w.r.t. a given closeness measure. Such sets are called granular approximations. In this article, we introduce the concepts of disjoint and adjacent granules and we examine how the new definitions affect the granular approximations. First, we show that the new concepts are important for binary classification problems since they help to keep decision regions separated (disjoint granules) and at the same time to cover as much as possible of the attribute space (adjacent granules). Later, we consider granular approximations for multi-class classification problems leading to the definition of a multi-class granular approximation. Finally, we show how to efficiently calculate multi-class granular approximations for Łukasiewicz fuzzy connectives. We also provide graphical illustrations for a better understanding of the introduced concepts.
31.10.2023
108765-1 - 108765-16
Article Number: 108765
140
3,9 [Lista 2022]