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)

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 languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages294-304
Number of pages11
Publication statusPublished - 2005 Dec 1
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

Other

Other2nd International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2005
CountryJapan
CityTsukuba
Period05/7/2505/7/27

Fingerprint

Genetic programming
Genetic Programming
Cancer
Ensemble
Classifiers
Classification Rules
Prediction
Classifier
Cancer Classification
Ensemble Classifier
Gene Expression Profile
Bioinformatics
Gene expression
Usability
Fusion
Fusion reactions
High Performance
Experiment
Experiments

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Hong, J. H., & Cho, S. B. (2005). Cancer prediction using diversity-based ensemble genetic programming. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 294-304). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3558 LNAI).
Hong, Jin Hyuk ; Cho, Sung Bae. / Cancer prediction using diversity-based ensemble genetic programming. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3558 LNAI, pp. 294-304, 2nd International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2005, Tsukuba, Japan, 05/7/25.

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

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 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 proceedingConference contribution

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Hong JH, Cho SB. Cancer prediction using diversity-based ensemble genetic programming. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2005. p. 294-304. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).