COMPUTATIONAL SEMANTIC REPRESENTATION GUARANTEES INTERPRETABILITY OF FUZZY RULE BASED CLASSIFIER

Phạm Đình Phong; Hoàng Văn Thông; Nguyễn Đức Dư

doi:10.34238/tnu-jst.6566

COMPUTATIONAL SEMANTIC REPRESENTATION GUARANTEES INTERPRETABILITY OF FUZZY RULE BASED CLASSIFIER

About this article

Received: 26/09/22 Revised: 19/10/22 Published: 20/10/22

Authors

1. Pham Dinh Phong, University of Transport and Communications
2. Hoang Van Thong , University of Transport and Communications
3. Nguyen Duc Du, University of Transport and Communications

Abstract

The fuzzy rule-based classifier design methods have been widely studied by the research community due to many practical applications in the real life. The quality of a classifier clearly depends on the semantic representations of linguistic words in the rule bases. Hedge algebra allows to the creation of a formal formalism for designing the fuzzy sets-based computational semantics of linguistic words from their inherent semantics. However, the existing design methods of fuzzy sets-based computational semantics of linguistic words do not guarantee the interpretability of the fuzzy rule-based classifiers. Specifically, the designed multi-granularity representation does not retain the generality-specificity relation of linguistic terms. This paper presents a fuzzy sets-based computational semantic representation that guarantees the interpretability of the fuzzy rule-based classifier. Experimental results on 23 real-world datasets have shown that the proposed method gives better classification accuracy while not increasing the complexity of the fuzzy rule-based systems in comparison with the existing methods.

Keywords

Hedge algebras; Order-based semantics; Classifier; Interpretability; Fuzzy rule-based systems

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DOI: https://doi.org/10.34238/tnu-jst.6566

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