A Novel Multiclass Classification Method with Gene Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Huang:2009:WISM,

• author = "Jiangtao Huang and Chuang Deng",
• title = "A Novel Multiclass Classification Method with Gene Expression Programming",
• booktitle = "International Conference on Web Information Systems and Mining, WISM 2009",
• year = "2009",
• month = nov,
• pages = "139--143",
• keywords = "genetic algorithms, genetic programming, computer programs, data mining, eigenvalue centroid, eigenvalue power function, gene expression programming, genotype-phenotype genetic algorithm, linear chromosomes, machine learning algorithms, multiclass classification method, data mining, eigenvalues and eigenfunctions, learning (artificial intelligence)",
• DOI = "doi:10.1109/WISM.2009.36",
• abstract = "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.",
• notes = "Also known as \cite{5369449}",

}

Genetic Programming entries for Jiangtao Huang Chuang Deng

Citations