Genetic Programming Based Ensemble System for Microarray Data Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Kun-Hong Liu and Muchenxuan Tong and Shu-Tong Xie and 
                 Vincent To Yee Ng",
  title =        "Genetic Programming Based Ensemble System for
                 Microarray Data Classification",
  journal =      "Computational and Mathematical Methods in Medicine",
  year =         "2015",
  volume =       "2015",
  pages =        "Article ID 193406",
  keywords =     "genetic algorithms, genetic programming",
  publisher =    "Hindawi Publishing Corporation",
  bibsource =    "OAI-PMH server at",
  language =     "en",
  oai =          "",
  rights =       "Copyright 2015 Kun-Hong Liu et al.; This is an open
                 access article distributed under the Creative Commons
                 Attribution License, which permits unrestricted use,
                 distribution, and reproduction in any medium, provided
                 the original work is properly cited.",
  URL =          "",
  DOI =          "doi:10.1155/2015/193406",
  abstract =     "Recently, more and more machine learning techniques
                 have been applied to microarray data analysis. The aim
                 of this study is to propose a genetic programming (GP)
                 based new ensemble system (named GPES), which can be
                 used to effectively classify different types of
                 cancers. Decision trees are deployed as base
                 classifiers in this ensemble framework with three
                 operators: Min, Max, and Average. Each individual of
                 the GP is an ensemble system, and they become more and
                 more accurate in the evolutionary process. The feature
                 selection technique and balanced subsampling technique
                 are applied to increase the diversity in each ensemble
                 system. The final ensemble committee is selected by a
                 forward search algorithm, which is shown to be capable
                 of fitting data automatically. The performance of GPES
                 is evaluated using five binary class and six multiclass
                 microarray datasets, and results show that the
                 algorithm can achieve better results in most cases
                 compared with some other ensemble systems. By using
                 elaborate base classifiers or applying other sampling
                 techniques, the performance of GPES may be further

Genetic Programming entries for Kun-Hong Liu MuChenxuan Tong Shu-Tong Xie Vincent To Yee Ng