Nonlinear Classification by Genetic Algorithm with Signed Fuzzy Measure

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By Michael Hempel Tue, 01/30/2018 - 11:00

Publication Type:

Conference Paper

Authors:

Honggang Wang; Fang, Hua; Sharif, Hamid; Zhenyuan Wang

Source:

Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007. IEEE International (2007)

Keywords:

Choquet integral, discrete misclassification rate, fuzzy set theory, genetic algorithm, genetic algorithms, signed fuzzy measure

Abstract:

In this paper, we propose a new nonlinear classifier based on a generalized Choquet integral with signed fuzzy measures to enhance the classification power by capturing all possible interactions among two or more attributes. A special genetic algorithm is designed to implement this classification optimization with fast convergence. Instead of using a discrete misclassification rate, the objective function to be optimized in this research is a continuous Choquet distance with a penalty coefficient for misclassified points. The numerical experiment shows that the special genetic algorithm effectively solves the nonlinear classification problem and this nonlinear classifier accurately identifies classes.