A Novel Multiclass Classification Method with Gene Expression Programming

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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.