Spectral clustering and feature selection for microarray data

Darío García-García; Raúl Santos-Rodríguez

doi:10.1109/ICMLA.2009.86

Spectral clustering and feature selection for microarray data

Darío García-García^*, Raúl Santos-Rodríguez

^*Corresponding author for this work

Department of Engineering Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

8 Citations (Scopus)

Abstract

Microarray datasets comprise a large number of gene expression values and a relatively small number of samples. Feature selection algorithms are very useful in these situations in order to find a compact subset of informative features. We propose a redundancy control method for algorithms in the recently proposed SPEC family of spectral-based feature selection algorithms. This method is applied to find relevant genes in order to cluster samples corresponding to three kinds of cancer: lung, breast and colon.

Original language	English
Title of host publication	8th International Conference on Machine Learning and Applications, ICMLA 2009
Pages	425-428
Number of pages	4
DOIs	https://doi.org/10.1109/ICMLA.2009.86
Publication status	Published - 2009
Event	8th International Conference on Machine Learning and Applications, ICMLA 2009 - Miami Beach, FL, United States Duration: 13 Dec 2009 → 15 Dec 2009

Publication series

Name	8th International Conference on Machine Learning and Applications, ICMLA 2009

Conference

Conference	8th International Conference on Machine Learning and Applications, ICMLA 2009
Country/Territory	United States
City	Miami Beach, FL
Period	13/12/09 → 15/12/09

Bibliographical note

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Copyright 2010 Elsevier B.V., All rights reserved.

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title = "Spectral clustering and feature selection for microarray data",

abstract = "Microarray datasets comprise a large number of gene expression values and a relatively small number of samples. Feature selection algorithms are very useful in these situations in order to find a compact subset of informative features. We propose a redundancy control method for algorithms in the recently proposed SPEC family of spectral-based feature selection algorithms. This method is applied to find relevant genes in order to cluster samples corresponding to three kinds of cancer: lung, breast and colon.",

author = "Dar{\'i}o Garc{\'i}a-Garc{\'i}a and Ra{\'u}l Santos-Rodr{\'i}guez",

year = "2009",

doi = "10.1109/ICMLA.2009.86",

language = "English",

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series = "8th International Conference on Machine Learning and Applications, ICMLA 2009",

pages = "425--428",

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}

García-García, D & Santos-Rodríguez, R 2009, Spectral clustering and feature selection for microarray data. in 8th International Conference on Machine Learning and Applications, ICMLA 2009., 5381488, 8th International Conference on Machine Learning and Applications, ICMLA 2009, pp. 425-428, 8th International Conference on Machine Learning and Applications, ICMLA 2009, Miami Beach, FL, United States, 13/12/09. https://doi.org/10.1109/ICMLA.2009.86

Spectral clustering and feature selection for microarray data. / García-García, Darío; Santos-Rodríguez, Raúl.
8th International Conference on Machine Learning and Applications, ICMLA 2009. 2009. p. 425-428 5381488 (8th International Conference on Machine Learning and Applications, ICMLA 2009).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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AU - Santos-Rodríguez, Raúl

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M3 - Conference Contribution (Conference Proceeding)

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SN - 9780769539263

T3 - 8th International Conference on Machine Learning and Applications, ICMLA 2009

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BT - 8th International Conference on Machine Learning and Applications, ICMLA 2009

T2 - 8th International Conference on Machine Learning and Applications, ICMLA 2009

Y2 - 13 December 2009 through 15 December 2009

ER -

Spectral clustering and feature selection for microarray data

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