9th Joint International Conference on Information Sciences (JCIS-06)

A Novel Feature Selection for Gene Expression Data

Authors
Cheng-Hong Yang 0, Li-Yeh Chuang, Chung-Jui Tu, Hsueh-Wei Chang
Corresponding Author
Cheng-Hong Yang
0National Kaohsiung University of Applied Sciences
Available Online October 2006.
DOI
https://doi.org/10.2991/jcis.2006.199How to use a DOI?
Keywords
Gene Expression Data, Particle Swarm Optimization, Support Vector Machines, Kernel-Adatron, One-Versus-Rest.
Abstract
The feature selection process can be considered a problem of global combinatorial optimization in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in an acceptable classification accuracy. Therefore, a good feature selection method based on the number of features investigated for sample classification is needed in order to speed up the processing rate, predictive accuracy, and to avoid incomprehensibility. In this paper, particle swarm optimization (PSO) is used to implement a feature selection, and the K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) serves as an evaluator of PSO. The support vector machines (SVMs) with the one-versus-rest method serve as a classifier for the classification problem. Experimental results show that our method simplifies features effectively and obtains a higher classification accuracy compared to the other classification methods from the literature.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
9th Joint International Conference on Information Sciences (JCIS-06)
Part of series
Advances in Intelligent Systems Research
Publication Date
October 2006
ISBN
978-90-78677-01-7
DOI
https://doi.org/10.2991/jcis.2006.199How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Cheng-Hong Yang
AU  - Li-Yeh Chuang
AU  - Chung-Jui Tu
AU  - Hsueh-Wei Chang
PY  - 2006/10
DA  - 2006/10
TI  - A Novel Feature Selection for Gene Expression Data
BT  - 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
UR  - https://doi.org/10.2991/jcis.2006.199
DO  - https://doi.org/10.2991/jcis.2006.199
ID  - Yang2006/10
ER  -