Ensemble neural networks with novel gene-subsets for multiclass cancer classification

Jin Hyuk Hong; Sung Bae Cho

doi:10.1007/978-3-540-69162-4_89

Ensemble neural networks with novel gene-subsets for multiclass cancer classification

Jin Hyuk Hong, Sung Bae Cho

College of Computing

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

Abstract

Multiclass gene selection and classification of cancer are rapidly gaining attention in recent years, while conventional rank-based gene selection methods depend on predefined ideal marker genes that basically devised for binary classification. In this paper, we propose a novel gene selection method based on a gene's local class discriminability, which does not require any ideal marker genes for multiclass classification. An ensemble classifier with multiple NNs is trained with the gene subsets. The Global Cancer Map (GCM) cancer dataset is used to verify the proposed method for comparisons with the conventional approaches.

Original language	English
Title of host publication	Neural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers
Pages	856-865
Number of pages	10
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-540-69162-4_89
Publication status	Published - 2008
Event	14th International Conference on Neural Information Processing, ICONIP 2007 - Kitakyushu, Japan Duration: 2007 Nov 13 → 2007 Nov 16

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	4985 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	14th International Conference on Neural Information Processing, ICONIP 2007
Country/Territory	Japan
City	Kitakyushu
Period	07/11/13 → 07/11/16

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

10.1007/978-3-540-69162-4_89

Cite this

Hong, J. H., & Cho, S. B. (2008). Ensemble neural networks with novel gene-subsets for multiclass cancer classification. In Neural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers (PART 2 ed., pp. 856-865). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4985 LNCS, No. PART 2). https://doi.org/10.1007/978-3-540-69162-4_89

Hong, Jin Hyuk ; Cho, Sung Bae. / Ensemble neural networks with novel gene-subsets for multiclass cancer classification. Neural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers. PART 2. ed. 2008. pp. 856-865 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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abstract = "Multiclass gene selection and classification of cancer are rapidly gaining attention in recent years, while conventional rank-based gene selection methods depend on predefined ideal marker genes that basically devised for binary classification. In this paper, we propose a novel gene selection method based on a gene's local class discriminability, which does not require any ideal marker genes for multiclass classification. An ensemble classifier with multiple NNs is trained with the gene subsets. The Global Cancer Map (GCM) cancer dataset is used to verify the proposed method for comparisons with the conventional approaches.",

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Hong, JH & Cho, SB 2008, Ensemble neural networks with novel gene-subsets for multiclass cancer classification. in Neural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 4985 LNCS, pp. 856-865, 14th International Conference on Neural Information Processing, ICONIP 2007, Kitakyushu, Japan, 07/11/13. https://doi.org/10.1007/978-3-540-69162-4_89

Ensemble neural networks with novel gene-subsets for multiclass cancer classification. / Hong, Jin Hyuk; Cho, Sung Bae.
Neural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers. PART 2. ed. 2008. p. 856-865 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4985 LNCS, No. PART 2).

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

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AB - Multiclass gene selection and classification of cancer are rapidly gaining attention in recent years, while conventional rank-based gene selection methods depend on predefined ideal marker genes that basically devised for binary classification. In this paper, we propose a novel gene selection method based on a gene's local class discriminability, which does not require any ideal marker genes for multiclass classification. An ensemble classifier with multiple NNs is trained with the gene subsets. The Global Cancer Map (GCM) cancer dataset is used to verify the proposed method for comparisons with the conventional approaches.

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Hong JH, Cho SB. Ensemble neural networks with novel gene-subsets for multiclass cancer classification. In Neural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers. PART 2 ed. 2008. p. 856-865. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-540-69162-4_89

Ensemble neural networks with novel gene-subsets for multiclass cancer classification

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