Improvement of Fuzzy Classification Systems Using Metaheuristic (PSO and ICA) with Dynamic Parameter Adaptation in Fuzzy Environment

Posted: 14 Jan 2019 Last revised: 10 Jun 2019

See all articles by Samaneh Asghari Kenarsari

Samaneh Asghari Kenarsari

University of Eyvanekey

AliMohammad Ahmadvand

University of Eyvanekey

Hossein Eghbali

University of Eyvanekey

Date Written: January 3, 2019

Abstract

In this research, fuzzy classification systems were improved by using some meta-heuristic methods with dynamic adaptation parameters. These methods were used for a set of data wherein membership functions and rules in fuzzy system are optimized by using meta-heuristic methods. In fuzzy classification method, being in different classes is assessed through membership functions which are in 0 to 1 interval. Each element belongs to the class where membership coefficient is higher. By assuming uncertainty in this research, a more realistic model for problem was made so that answers obtained from this model have more capability to be implemented. In this research, we improved fuzzy classification by using PSO1 and ICA2 methods with fuzzy parameters, which not only reduce classification error, but also provide a comparison between efficiency of fuzzy PSO and fuzzy ICA. In this research, data are not just for testing and these fuzzy algorithms were used for IRIS database (this database has 3 different classes for Iris flower and 150 data which each datum has four characteristics). We came to this conclusion that fuzzy classification systems can be improved by using fuzzy meta-heuristic methods and fuzzy ICA algorithm has more convergence velocity than fuzzy PSO algorithm.

Keywords: Fuzzy Classification, Optimization, Dynamic parameter, meta heuristic algorithm, Fuzzy logic

JEL Classification: c02

Suggested Citation

Kenarsari, Samaneh Asghari and Ahmadvand, AliMohammad and Eghbali, Hossein, Improvement of Fuzzy Classification Systems Using Metaheuristic (PSO and ICA) with Dynamic Parameter Adaptation in Fuzzy Environment (January 3, 2019). Available at SSRN: https://ssrn.com/abstract=3309780 or http://dx.doi.org/10.2139/ssrn.3309780

Samaneh Asghari Kenarsari

University of Eyvanekey

Eyvanekey
Iran

AliMohammad Ahmadvand

University of Eyvanekey

Eyvanekey
Iran

Hossein Eghbali (Contact Author)

University of Eyvanekey ( email )

www.eyc.ac.ir
www.eyc.ac.ir
Eyvanekey, semnan
Iran
+982334521563 (Phone)
+982334521562 (Fax)

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