A genetic design of linguistic terms for fuzzy rule based classifiers

• Published in: International journal of approximate reasoning Vol. 54; no. 1; pp. 1 - 21
• Main Authors: Nguyen, Cat Ho, Pedrycz, Witold, Duong, Thang Long, Tran, Thai Son
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.01.2013; Elsevier
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Data processing. List processing. Character string processing; Evolutionary optimization; Exact sciences and technology; Fuzzy classification; Fuzzy rules based classification systems; Hedge algebras based semantics of terms; Linguistic terms design; Logical, boolean and switching functions; Memory organisation. Data processing; Software; Theoretical computing; Fuzzy rules based classification systems; Linguistic terms design; Hedge algebras based semantics of terms; Fuzzy classification; Evolutionary optimization; Membership function; Semantic analysis; Evolutionary algorithm; Expert system; Fuzzy set; information granule; Optimization; Optimal design; Fuzzy logic; Semantics; Classification; Linguistics; Syntax; Artificial intelligence; Inference rule
• ISSN: 0888-613X; 1873-4731
• DOI: 10.1016/j.ijar.2012.07.007

The determination of fuzzy information granules including the estimation of their membership functions play a significant role in fuzzy system design as well as in the design of fuzzy rule based classifiers (FRBCSs). However, although linguistic terms are fundamental elements in the process of elucidating expert’s knowledge, the problem of linguistic term design along with their fuzzy-set-based semantics has not been fully addressed, since term-sets of attributes have not been interpreted as a formalized structure. Thus, the essential relationship between linguistic terms, as syntax, and the constructed fuzzy sets, as their quantitative semantics, or in other words, the problem of the natural semantics of terms behind the linguistic literal has not been addressed. In this paper, we introduce the problem of the design of optimal linguistic terms and propose a method of the design of FRBCSs which may incorporate with the design of linguistic terms to ensure that the presence of linguistic literals are supported not only by data but also by their natural semantics. It is shown that this problem plays a primordial role in enhancing the performance and the interpretability of the designed FRBCSs and helps striking a better balance between the generality and the specificity of the desired fuzzy rule bases for fuzzy classification problems. A series of experiments concerning 17 Machine Learning datasets is reported.