Global Top-Scoring Pair Decision Tree for Gene Expression Data Analysis

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

@InProceedings{czajkowski:2013:EuroGP,
  author =       "Marcin Czajkowski and Marek Kretowski",
  title =        "Global Top-Scoring Pair Decision Tree for Gene
                 Expression Data Analysis",
  booktitle =    "Proceedings of the 16th European Conference on Genetic
                 Programming, EuroGP 2013",
  year =         "2013",
  month =        "3-5 " # apr,
  editor =       "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and 
                 A. Sima Uyar and Bin Hu",
  series =       "LNCS",
  volume =       "7831",
  publisher =    "Springer Verlag",
  address =      "Vienna, Austria",
  pages =        "229--240",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 algorithms, decision tree, top-scoring pair,
                 classification, gene expression, micro-array",
  isbn13 =       "978-3-642-37206-3",
  DOI =          "doi:10.1007/978-3-642-37207-0_20",
  abstract =     "Extracting knowledge from gene expression data is
                 still a major challenge. Relative expression algorithms
                 use the ordering relationships for a small collection
                 of genes and are successfully applied for micro-array
                 classification. However, searching for all possible
                 subsets of genes requires a significant number of
                 calculations, assumptions and limitations. In this
                 paper we propose an evolutionary algorithm for global
                 induction of top-scoring pair decision trees. We have
                 designed several specialised genetic operators that
                 search for the best tree structure and the splits in
                 internal nodes which involve pairwise comparisons of
                 the gene expression values. Preliminary validation
                 performed on real-life micro-array datasets is
                 promising as the proposed solution is highly
                 competitive to other relative expression algorithms and
                 allows exploring much larger solution space.",
  notes =        "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in
                 conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013
                 and EvoApplications2013",
}

Genetic Programming entries for Marcin Czajkowski Marek Kretowski

Citations