Evolutionary computation for optimal ensemble classifier in lymphoma cancer classification

Chanho Park; Sung Bae Cho

doi:10.1007/978-3-540-39592-8_74

Evolutionary computation for optimal ensemble classifier in lymphoma cancer classification

Chanho Park, Sung Bae Cho

Department of Computer Science

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

12 Citations (Scopus)

Abstract

Owing to the development of DNA microarray technologies, it is possible to get thousands of expression levels of genes at once. If we make the effective classification system with such acquired data, we can predict the class of new sample, whether it is normal or patient. For the classification system, we can use many feature selection methods and classifiers, but a method cannot be superior to the others absolutely for feature selection or classification. Ensemble classifier has been using to yield improved performance in this situation, but it is almost impossible to get all ensemble results, if there are many feature selection methods and classifiers to be used for ensemble. In this paper, we propose GA based method for searching optimal ensemble of feature-classifier pairs on Lymphoma cancer dataset. We have used two ensemble methods, and GA finds optimal ensemble very efficiently.

Original language	English
Title of host publication	Foundations of Intelligent Systems - 14th International Symposium, ISMIS 2003, Proceedings
Editors	Ning Zhong, Zbigniew W. Ras, Shusaku Tsumoto, Einoshin Suzuki
Publisher	Springer Verlag
Pages	521-530
Number of pages	10
ISBN (Print)	3540202560, 9783540202561
DOIs	https://doi.org/10.1007/978-3-540-39592-8_74
Publication status	Published - 2003
Event	14th International Symposium on Methodologies for Intelligent Systems, ISMIS 2003 - Maebashi City, Japan Duration: 2003 Oct 28 → 2003 Oct 31

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2871
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	14th International Symposium on Methodologies for Intelligent Systems, ISMIS 2003
Country/Territory	Japan
City	Maebashi City
Period	03/10/28 → 03/10/31

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2003.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-540-39592-8_74

Cite this

Park, C., & Cho, S. B. (2003). Evolutionary computation for optimal ensemble classifier in lymphoma cancer classification. In N. Zhong, Z. W. Ras, S. Tsumoto, & E. Suzuki (Eds.), Foundations of Intelligent Systems - 14th International Symposium, ISMIS 2003, Proceedings (pp. 521-530). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2871). Springer Verlag. https://doi.org/10.1007/978-3-540-39592-8_74

Park, Chanho ; Cho, Sung Bae. / Evolutionary computation for optimal ensemble classifier in lymphoma cancer classification. Foundations of Intelligent Systems - 14th International Symposium, ISMIS 2003, Proceedings. editor / Ning Zhong ; Zbigniew W. Ras ; Shusaku Tsumoto ; Einoshin Suzuki. Springer Verlag, 2003. pp. 521-530 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Owing to the development of DNA microarray technologies, it is possible to get thousands of expression levels of genes at once. If we make the effective classification system with such acquired data, we can predict the class of new sample, whether it is normal or patient. For the classification system, we can use many feature selection methods and classifiers, but a method cannot be superior to the others absolutely for feature selection or classification. Ensemble classifier has been using to yield improved performance in this situation, but it is almost impossible to get all ensemble results, if there are many feature selection methods and classifiers to be used for ensemble. In this paper, we propose GA based method for searching optimal ensemble of feature-classifier pairs on Lymphoma cancer dataset. We have used two ensemble methods, and GA finds optimal ensemble very efficiently.",

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Park, C & Cho, SB 2003, Evolutionary computation for optimal ensemble classifier in lymphoma cancer classification. in N Zhong, ZW Ras, S Tsumoto & E Suzuki (eds), Foundations of Intelligent Systems - 14th International Symposium, ISMIS 2003, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2871, Springer Verlag, pp. 521-530, 14th International Symposium on Methodologies for Intelligent Systems, ISMIS 2003, Maebashi City, Japan, 03/10/28. https://doi.org/10.1007/978-3-540-39592-8_74

Evolutionary computation for optimal ensemble classifier in lymphoma cancer classification. / Park, Chanho; Cho, Sung Bae.

Foundations of Intelligent Systems - 14th International Symposium, ISMIS 2003, Proceedings. ed. / Ning Zhong; Zbigniew W. Ras; Shusaku Tsumoto; Einoshin Suzuki. Springer Verlag, 2003. p. 521-530 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2871).

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

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AU - Cho, Sung Bae

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2003.

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N2 - Owing to the development of DNA microarray technologies, it is possible to get thousands of expression levels of genes at once. If we make the effective classification system with such acquired data, we can predict the class of new sample, whether it is normal or patient. For the classification system, we can use many feature selection methods and classifiers, but a method cannot be superior to the others absolutely for feature selection or classification. Ensemble classifier has been using to yield improved performance in this situation, but it is almost impossible to get all ensemble results, if there are many feature selection methods and classifiers to be used for ensemble. In this paper, we propose GA based method for searching optimal ensemble of feature-classifier pairs on Lymphoma cancer dataset. We have used two ensemble methods, and GA finds optimal ensemble very efficiently.

AB - Owing to the development of DNA microarray technologies, it is possible to get thousands of expression levels of genes at once. If we make the effective classification system with such acquired data, we can predict the class of new sample, whether it is normal or patient. For the classification system, we can use many feature selection methods and classifiers, but a method cannot be superior to the others absolutely for feature selection or classification. Ensemble classifier has been using to yield improved performance in this situation, but it is almost impossible to get all ensemble results, if there are many feature selection methods and classifiers to be used for ensemble. In this paper, we propose GA based method for searching optimal ensemble of feature-classifier pairs on Lymphoma cancer dataset. We have used two ensemble methods, and GA finds optimal ensemble very efficiently.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Park C, Cho SB. Evolutionary computation for optimal ensemble classifier in lymphoma cancer classification. In Zhong N, Ras ZW, Tsumoto S, Suzuki E, editors, Foundations of Intelligent Systems - 14th International Symposium, ISMIS 2003, Proceedings. Springer Verlag. 2003. p. 521-530. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-540-39592-8_74

Evolutionary computation for optimal ensemble classifier in lymphoma cancer classification

Abstract

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Bibliographical note

All Science Journal Classification (ASJC) codes

UN SDGs

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