A novel ensemble algorithm for tumor classification

Zhan Li Sun; Han Wang; Wai Shing Lau; Gerald Seet; Danwei Wang; Kin Man Lam

doi:10.1007/978-3-642-39068-5-36

A novel ensemble algorithm for tumor classification

Zhan Li Sun, Han Wang, Wai Shing Lau, Gerald Seet, Danwei Wang, Kin Man Lam

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Abstract

From the viewpoint of image processing, a spectral feature-based TLS (Tikhonov-regularized least-squares) ensemble algorithm is proposed for tumor classification using gene expression data. In the TLS model, a test sample is represented as a linear combination of atoms of an overcomplete dictionary. Two types of dictionaries, spectral feature-based eigenassays and spectral feature-based metasamples, are proposed for the TLS model. Experimental results on standard databases demonstrate the feasibility and effectiveness of the proposed method.

Original language	English
Title of host publication	Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings
Pages	292-298
Number of pages	7
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-39068-5-36
Publication status	Published - 1 Aug 2013
Event	10th International Symposium on Neural Networks, ISNN 2013 - Dalian, China Duration: 4 Jul 2013 → 6 Jul 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	7952 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Symposium on Neural Networks, ISNN 2013
Country/Territory	China
City	Dalian
Period	4/07/13 → 6/07/13

Keywords

gene expression data
Tikhonov- regularized least-squares model
Tumor classification

ASJC Scopus subject areas

General Computer Science
Theoretical Computer Science

More information

10.1007/978-3-642-39068-5-36

Cite this

Sun, Z. L., Wang, H., Lau, W. S., Seet, G., Wang, D., & Lam, K. M. (2013). A novel ensemble algorithm for tumor classification. In Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings (PART 2 ed., pp. 292-298). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7952 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-39068-5-36

Sun, Zhan Li ; Wang, Han ; Lau, Wai Shing et al. / A novel ensemble algorithm for tumor classification. Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings. PART 2. ed. 2013. pp. 292-298 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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title = "A novel ensemble algorithm for tumor classification",

abstract = "From the viewpoint of image processing, a spectral feature-based TLS (Tikhonov-regularized least-squares) ensemble algorithm is proposed for tumor classification using gene expression data. In the TLS model, a test sample is represented as a linear combination of atoms of an overcomplete dictionary. Two types of dictionaries, spectral feature-based eigenassays and spectral feature-based metasamples, are proposed for the TLS model. Experimental results on standard databases demonstrate the feasibility and effectiveness of the proposed method.",

keywords = "gene expression data, Tikhonov- regularized least-squares model, Tumor classification",

author = "Sun, {Zhan Li} and Han Wang and Lau, {Wai Shing} and Gerald Seet and Danwei Wang and Lam, {Kin Man}",

year = "2013",

month = aug,

day = "1",

doi = "10.1007/978-3-642-39068-5-36",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings",

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Sun, ZL, Wang, H, Lau, WS, Seet, G, Wang, D & Lam, KM 2013, A novel ensemble algorithm for tumor classification. in Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 7952 LNCS, pp. 292-298, 10th International Symposium on Neural Networks, ISNN 2013, Dalian, China, 4/07/13. https://doi.org/10.1007/978-3-642-39068-5-36

A novel ensemble algorithm for tumor classification. / Sun, Zhan Li; Wang, Han; Lau, Wai Shing et al.
Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings. PART 2. ed. 2013. p. 292-298 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7952 LNCS, No. PART 2).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - From the viewpoint of image processing, a spectral feature-based TLS (Tikhonov-regularized least-squares) ensemble algorithm is proposed for tumor classification using gene expression data. In the TLS model, a test sample is represented as a linear combination of atoms of an overcomplete dictionary. Two types of dictionaries, spectral feature-based eigenassays and spectral feature-based metasamples, are proposed for the TLS model. Experimental results on standard databases demonstrate the feasibility and effectiveness of the proposed method.

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Sun ZL, Wang H, Lau WS, Seet G, Wang D, Lam KM. A novel ensemble algorithm for tumor classification. In Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings. PART 2 ed. 2013. p. 292-298. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-39068-5-36

A novel ensemble algorithm for tumor classification

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