Hybrid Algorithm for Fuzzy Model Parameter Estimation Based on Genetic Algorithm and Derivative Based Methods

Lavygina, A.; Hodashinsky, I. A.

doi:10.5220/0003690605130515

Cited by 2 publications

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“…The structure of the classifier is most often formed with the use of clustering methods designed to identify the data structure and build information granules that may be related to linguistic terms [2]. Parameters of fuzzy rules can be optimized using conventional approaches based on calculation of derivatives or with the help of metaheuristics methods [6].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Classifier with Feature Selection Based on the Gravitational Search Algorithm

et al. 2018

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This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric and asymmetric structure of a transfer function, which is responsible to map a continuous search space to a binary search space. A new method for design of a fuzzy-rule-based classifier using metaheuristics called Gravitational Search Algorithm (GSA) is discussed. The paper identifies three basic stages of the classifier construction: feature selection, creating of a fuzzy rule base and optimization of the antecedent parameters of rules. At the first stage, several feature subsets are obtained by using the wrapper scheme on the basis of the binary GSA. Creating fuzzy rules is a serious challenge in designing the fuzzy-rule-based classifier in the presence of high-dimensional data. The classifier structure is formed by the rule base generation algorithm by using minimum and maximum feature values. The optimal fuzzy-rule-based parameters are extracted from the training data using the continuous GSA. The classifier performance is tested on real-world KEEL (Knowledge Extraction based on Evolutionary Learning) datasets. The results demonstrate that highly accurate classifiers could be constructed with relatively few fuzzy rules and features.

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Section: Introductionmentioning

confidence: 99%