Cancer prediction using diversity-based ensemble genetic programming

Jin Hyuk Hong; Sung Bae Cho

doi:10.1007/11526018_29

Cancer prediction using diversity-based ensemble genetic programming

Jin Hyuk Hong, Sung Bae Cho

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

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.

Original language	English
Title of host publication	Modeling Decisions for Artificial Intelligence - Second International Conference, MDAI 2005, Proceedings
Publisher	Springer Verlag
Pages	294-304
Number of pages	11
ISBN (Print)	3540278710, 9783540278719
DOIs	https://doi.org/10.1007/11526018_29
Publication status	Published - 2005
Event	2nd International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2005 - Tsukuba, Japan Duration: 2005 Jul 25 → 2005 Jul 27

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3558 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	2nd International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2005
Country	Japan
City	Tsukuba
Period	05/7/25 → 05/7/27

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

UN SDGs

This output contributes to the following Sustainable Development Goal(s)

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10.1007/11526018_29

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Hong, J. H., & Cho, S. B. (2005). Cancer prediction using diversity-based ensemble genetic programming. In Modeling Decisions for Artificial Intelligence - Second International Conference, MDAI 2005, Proceedings (pp. 294-304). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3558 LNAI). Springer Verlag. https://doi.org/10.1007/11526018_29

Hong, Jin Hyuk ; Cho, Sung Bae. / Cancer prediction using diversity-based ensemble genetic programming. Modeling Decisions for Artificial Intelligence - Second International Conference, MDAI 2005, Proceedings. Springer Verlag, 2005. pp. 294-304 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Hong, JH & Cho, SB 2005, Cancer prediction using diversity-based ensemble genetic programming. in Modeling Decisions for Artificial Intelligence - Second International Conference, MDAI 2005, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3558 LNAI, Springer Verlag, pp. 294-304, 2nd International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2005, Tsukuba, Japan, 05/7/25. https://doi.org/10.1007/11526018_29

Cancer prediction using diversity-based ensemble genetic programming. / Hong, Jin Hyuk; Cho, Sung Bae.

Modeling Decisions for Artificial Intelligence - Second International Conference, MDAI 2005, Proceedings. Springer Verlag, 2005. p. 294-304 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3558 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - 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.

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Hong JH, Cho SB. Cancer prediction using diversity-based ensemble genetic programming. In Modeling Decisions for Artificial Intelligence - Second International Conference, MDAI 2005, Proceedings. Springer Verlag. 2005. p. 294-304. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/11526018_29

Cancer prediction using diversity-based ensemble genetic programming

Abstract

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All Science Journal Classification (ASJC) codes

UN SDGs

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