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A Novel Multiclass Classification Method with Gene Expression Programming

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2 Author(s)
Jiangtao Huang ; Inst. of Image & Graphics, Sichuan Univ., Chengdu, China ; Chuang Deng

Classification is one of the fundamental tasks of data mining, and many machine learning algorithms are inherently designed for binary (two-class) decision problems. Gene expression programming (GEP) is a genotype/phenotype genetic algorithm that evolves computer programs of different sizes and shapes (expression trees) encoded in linear chromosomes of fixed length. In this paper, we propose a novel method for multiclass classification by using GEP, a new hybrid of genetic algorithms (GAs) and genetic programming (GP). Different to the common method of formulating a multiclass classification problem as multiple two-class problems, we construct a novel multiclass classification by using eigenvalue centroid of each class and eigenvalue-power function. Experimental results on two real data sets demonstrate that method is able to achieve a preferable solution.

Published in:

Web Information Systems and Mining, 2009. WISM 2009. International Conference on

Date of Conference:

7-8 Nov. 2009