By Topic

Gene selection for cancer classification using bootstrapped genetic algorithms and support vector machines

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
X. -W. Chen ; Dept. of Electr. & Comput. Eng., California State Univ., Northridge, CA, USA

The gene expression data obtained from microarrays have shown useful in cancer classification. DNA microarray data have extremely high dimensionality compared to the small number of available samples. In this paper, we propose a novel system for selecting a set of genes for cancer classification. This system is based on a linear support vector machine and a genetic algorithm. To overcome the problem of the small size of training samples, bootstrap methods are combined into genetic search. Two databases are considered: the colon cancer database and the leukemia database. Our experimental results show that the proposed method is capable of finding genes that discriminate between normal cells and cancer cells and generalizes well.

Published in:

Bioinformatics Conference, 2003. CSB 2003. Proceedings of the 2003 IEEE

Date of Conference:

11-14 Aug. 2003