DNA gene expression classification with ensemble classifiers optimized by speciated genetic algorithm

Kyung Joong Kim, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationPattern Recognition and Machine Intelligence - First International Conference, PReMI 2005, Proceedings
Pages649-653
Number of pages5
Publication statusPublished - 2005 Dec 1
Event1st International Conference on Pattern Recognition and Machine Intelligence, PReMI 2005 - Kolkata, India
Duration: 2005 Dec 202005 Dec 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3776 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Conference on Pattern Recognition and Machine Intelligence, PReMI 2005
CountryIndia
CityKolkata
Period05/12/2005/12/22

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Kim, K. J., & Cho, S. B. (2005). DNA gene expression classification with ensemble classifiers optimized by speciated genetic algorithm. In Pattern Recognition and Machine Intelligence - First International Conference, PReMI 2005, Proceedings (pp. 649-653). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3776 LNCS).