Cancer prediction using diversity-based ensemble genetic programming

Jin Hyuk Hong, Sung Bae Cho

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

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationModeling Decisions for Artificial Intelligence - Second International Conference, MDAI 2005, Proceedings
PublisherSpringer Verlag
Number of pages11
ISBN (Print)3540278710, 9783540278719
Publication statusPublished - 2005
Event2nd International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2005 - Tsukuba, Japan
Duration: 2005 Jul 252005 Jul 27

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3558 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other2nd International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2005

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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