A Novel Multiclass Classification Method with Gene Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@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