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

Cornelis Chris, Bollaert Henri

Annals of Computer Science and Information Systems

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2023

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Vol. 35

67--67

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.

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Hybridization of fuzzy sets and rough sets: achievements and opportunities

Cornelis Chris

Annals of Computer Science and Information Systems

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2022

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Vol. 30

7--14

EN

Fuzzy rough sets are the fruit of an intense and longlasting collaboration effort between fuzzy set theory and rough set theory. Seminal research on the hybridization originated in the late 1980's, and has inspired generations of researchers from around the globe to address both theoretical and practical challenges. In this paper, we gauge the state-of-the-art in this domain and identify opportunities for further development. In particular, we highlight the potential of fuzzy quantifiers in creating new robust fuzzy rough models, we advocate closer integration with granular computing as a stepping stone for designing rule induction algorithms, and we contemplate the role of fuzzy rough sets vis-à-vis explainable artificial intelligence.

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Fuzzy quantifier-based fuzzy rough sets

Theerens Adnan, Cornelis Chris

Annals of Computer Science and Information Systems

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2022

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Vol. 30

269--278

EN

In this paper we apply vague quantification to fuzzy rough sets to introduce fuzzy quantifier based fuzzy rough sets (FQFRS), an intuitive generalization of fuzzy rough sets. We show how several existing models fit in this generalization as well as how it inspires novel models that may improve these existing models. In addition, we introduce several new binary quantification models. Finally, we introduce an adaptation of FQFRS that allows seamless integration of outlier detection algorithms to enhance the robustness of the applications based on FQFRS.