DNA Gene Expression Classification with Ensemble Classifiers Optimized by Speciated Genetic Algorithm

  • Kyung-Joong Kim
  • Sung-Bae Cho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)


Accurate cancer classification is very important to cancer diagnosis and treatment. As molecular information is increasing for the cancer classification, a lot of techniques have been proposed and utilized to classify and predict the cancers from gene expression profiles. In this paper, we propose a method based on speciated evolution for the cancer classification. The optimal combination among several feature-classifier pairs from the various features and classifiers is evolutionarily searched using the deterministic crowding genetic algorithm. Experimental results demonstrate that the proposed method is more effective than the standard genetic algorithm and the fitness sharing genetic algorithm as well as the best single classifier to search the optimal ensembles for the cancer classification.


Ensemble Method Ensemble Classifier Cancer Classification Standard Genetic Algorithm Optimal Ensemble 
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  1. 1.
    Cho, S.–B., Won, H.–H.: Data mining for gene expression profiles from DNA microar-ray. Int. Journal of Software Engineering and Knowledge Engineering 13, 593–608 (2003)CrossRefGoogle Scholar
  2. 2.
    Tan, A.C., Gilbert, D.: Ensemble machine learning on gene expression data for cancer classification. Applied Bioinformatics 2, s75–s83 (2003)Google Scholar
  3. 3.
    Tsymbal, A., Puuronen, S.: Ensemble feature selection with the simple Bayesian classi-fication in medical diagnostics. In: Proc. of the 15th IEEE Symp. on Computer-Based Medical Systems, pp. 225–230 (2002)Google Scholar
  4. 4.
    Cho, S.-B., Ryu, J.-W.: Classifying gene expression data of cancer using classifier en-semble with mutually exclusive features. Proc. of the IEEE 90(11), 1744–1753 (2002)CrossRefGoogle Scholar
  5. 5.
    Kuncheva, L.I., Jain, L.C.: Designing classifier fusion systems by genetic algorithms. IEEE Transactions on Evolutionary Computation 4(4), 327–336 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Kyung-Joong Kim
    • 1
  • Sung-Bae Cho
    • 1
  1. 1.Department of Computer ScienceYonsei UniversitySeoulSouth Korea

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