Multi-class cancer classification with OVR-support vector machines selected by Naïve bayes classifier

Jin Hyuk Hong; Sung Bae Cho

doi:10.1007/11893295_18

Multi-class cancer classification with OVR-support vector machines selected by Naïve bayes classifier

Jin Hyuk Hong, Sung Bae Cho

Department of Computer Science

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

5 Citations (Scopus)

Abstract

Support vector machines (SVMs), originally designed for binary classification, have been applied for multi-class classification, where an effective fusion scheme is required for combining outputs from them and producing a final result. In this work, we propose a novel method in which the SVMs are generated with the one-vs-rest (OVR) scheme and dynamically organized by the naive Bayes classifiers (NBs). This method might break the ties that frequently occur when working with multi-class classification systems with OVR SVMs. More specifically, we use the Pearson correlation measure to select informative genes and reduce the dimensionality of gene expression profiles when constructing the NBs. The proposed method has been validated on GCM cancer dataset consisting of 14 types of tumors with 16, 063 gene expression levels and produced higher accuracy than other methods.

Original language	English
Title of host publication	Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings
Publisher	Springer Verlag
Pages	155-164
Number of pages	10
ISBN (Print)	3540464840, 9783540464846
DOIs	https://doi.org/10.1007/11893295_18
Publication status	Published - 2006
Event	13th International Conference on Neural Information Processing, ICONIP 2006 - Hong Kong, China Duration: 2006 Oct 3 → 2006 Oct 6

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4234 LNCS - III
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	13th International Conference on Neural Information Processing, ICONIP 2006
Country	China
City	Hong Kong
Period	06/10/3 → 06/10/6

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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10.1007/11893295_18

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Hong, J. H., & Cho, S. B. (2006). Multi-class cancer classification with OVR-support vector machines selected by Naïve bayes classifier. In Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings (pp. 155-164). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4234 LNCS - III). Springer Verlag. https://doi.org/10.1007/11893295_18

Hong, Jin Hyuk ; Cho, Sung Bae. / Multi-class cancer classification with OVR-support vector machines selected by Naïve bayes classifier. Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings. Springer Verlag, 2006. pp. 155-164 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Support vector machines (SVMs), originally designed for binary classification, have been applied for multi-class classification, where an effective fusion scheme is required for combining outputs from them and producing a final result. In this work, we propose a novel method in which the SVMs are generated with the one-vs-rest (OVR) scheme and dynamically organized by the naive Bayes classifiers (NBs). This method might break the ties that frequently occur when working with multi-class classification systems with OVR SVMs. More specifically, we use the Pearson correlation measure to select informative genes and reduce the dimensionality of gene expression profiles when constructing the NBs. The proposed method has been validated on GCM cancer dataset consisting of 14 types of tumors with 16, 063 gene expression levels and produced higher accuracy than other methods.",

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Hong, JH & Cho, SB 2006, Multi-class cancer classification with OVR-support vector machines selected by Naïve bayes classifier. in Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4234 LNCS - III, Springer Verlag, pp. 155-164, 13th International Conference on Neural Information Processing, ICONIP 2006, Hong Kong, China, 06/10/3. https://doi.org/10.1007/11893295_18

Multi-class cancer classification with OVR-support vector machines selected by Naïve bayes classifier. / Hong, Jin Hyuk; Cho, Sung Bae.

Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings. Springer Verlag, 2006. p. 155-164 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4234 LNCS - III).

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

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AB - Support vector machines (SVMs), originally designed for binary classification, have been applied for multi-class classification, where an effective fusion scheme is required for combining outputs from them and producing a final result. In this work, we propose a novel method in which the SVMs are generated with the one-vs-rest (OVR) scheme and dynamically organized by the naive Bayes classifiers (NBs). This method might break the ties that frequently occur when working with multi-class classification systems with OVR SVMs. More specifically, we use the Pearson correlation measure to select informative genes and reduce the dimensionality of gene expression profiles when constructing the NBs. The proposed method has been validated on GCM cancer dataset consisting of 14 types of tumors with 16, 063 gene expression levels and produced higher accuracy than other methods.

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Hong JH, Cho SB. Multi-class cancer classification with OVR-support vector machines selected by Naïve bayes classifier. In Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings. Springer Verlag. 2006. p. 155-164. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/11893295_18

Multi-class cancer classification with OVR-support vector machines selected by Naïve bayes classifier

Abstract

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