A genetic programming-based approach to the classification of multiclass microarray datasets

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Liu:2009:B,
  author =       "Kun-Hong Liu and Chun-Gui Xu",
  title =        "A genetic programming-based approach to the
                 classification of multiclass microarray datasets",
  journal =      "Bioinformatics",
  year =         "2009",
  volume =       "25",
  number =       "3",
  pages =        "331--337",
  keywords =     "genetic algorithms, genetic programming, lung cancer",
  DOI =          "doi:10.1093/bioinformatics/btn644",
  size =         "7 pages",
  abstract =     "MOTIVATION: Feature selection approaches have been
                 widely applied to deal with the small sample size
                 problem in the analysis of micro-array datasets. For
                 the multiclass problem, the proposed methods are based
                 on the idea of selecting a gene subset to distinguish
                 all classes. However, it will be more effective to
                 solve a multiclass problem by splitting it into a set
                 of two-class problems and solving each problem with a
                 respective classification system. RESULTS: We propose a
                 genetic programming (GP)-based approach to analyze
                 multiclass microarray datasets. Unlike the traditional
                 GP, the individual proposed in this article consists of
                 a set of small-scale ensembles, named as sub-ensemble
                 (denoted by SE). Each SE consists of a set of trees. In
                 application, a multiclass problem is divided into a set
                 of two-class problems, each of which is tackled by a SE
                 first. The SEs tackling the respective two-class
                 problems are combined to construct a GP individual, so
                 each individual can deal with a multiclass problem
                 directly. Effective methods are proposed to solve the
                 problems arising in the fusion of SEs, and a greedy
                 algorithm is designed to keep high diversity in SEs.
                 This GP is tested in five datasets. The results show
                 that the proposed method effectively implements the
                 feature selection and classification tasks.",
  notes =        "multi-tree (cf ADF) individual, one tree per
                 class.

                 Supplementary data are available at Bioinformatics
                 online.

                 School of Software, Xiamen University, Xiamen, Fujian,
                 361005, China PMID: 19088122 [PubMed - indexed for
                 MEDLINE]",
}

Genetic Programming entries for Kun-Hong Liu Chun-Gui Xu

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