Cancer Prediction Using Diversity-Based Ensemble Genetic Programming

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@InProceedings{conf/mdai/HongC05,
  title =        "Cancer Prediction Using Diversity-Based Ensemble
                 Genetic Programming",
  author =       "Jin-Hyuk Hong and Sung-Bae Cho",
  year =         "2005",
  pages =        "294--304",
  editor =       "Vicenc Torra and Yasuo Narukawa and Sadaaki Miyamoto",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3558",
  booktitle =    "Modeling Decisions for Artificial Intelligence, Second
                 International Conference, MDAI 2005, Proceedings",
  address =      "Tsukuba, Japan",
  month =        jul # " 25-27",
  bibdate =      "2005-07-18",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/mdai/mdai2005.html#HongC05",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-27871-0",
  DOI =          "doi:10.1007/11526018_29",
  abstract =     "Combining a set of classifiers has often been
                 exploited to improve the classification performance.
                 Accurate as well as diverse base classifiers are
                 prerequisite to construct a good ensemble classifier.
                 Therefore, estimating diversity among classifiers has
                 been widely investigated. This paper presents an
                 ensemble approach that combines a set of diverse rules
                 obtained by genetic programming. Genetic programming
                 generates interpretable classification rules, and
                 diversity among them is directly estimated. Finally,
                 several diverse rules are combined by a fusion method
                 to generate a final decision. The proposed method has
                 been applied to cancer classification using gene
                 expression profiles, which is one of the important
                 issues in bioinformatics. Experiments on several
                 popular cancer datasets have demonstrated the usability
                 of the method. High performance of the proposed method
                 has been obtained, and the accuracy has increased by
                 diversity among the base classification rules.",
}

Genetic Programming entries for Jin-Hyuk Hong Sung Bae Cho

Citations