By Topic

An Effective Microarray Data Classifier Based on Gene Expression Programming

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

5 Author(s)
Lei Duan ; Sch. of Comput. Sci., Sichuan Univ., Chengdu, China ; Changjie Tang ; Liang Tang ; Jie Zuo
more authors

Applying data mining algorithms to microarray data analysis is an interesting and promising work. Gene Expression Programming (GEP) is a new development of evolution computation. GEP performs global search and discover the classification discriminant with high accuracy. However, it is undesirable to apply GEP on microarray classification directly, since the evolution efficiency of GEP is low when the number of attributes of training data is huge. To solve this problem, the main contributions of this paper include: (1) analyzing the difficulties of applying GEP to classifying microarray data directly, (2) designing a novel method to select GEP terminals from genes of microarray data, (3) proposing a method of constructing GEP classifier committee to improve the classification accuracy, (4) demonstrating the effectiveness of proposed algorithms by extensive experiments on several microarray data. Compared with some typical classification methods, the accuracy is increased as high as 10.46% in average.

Published in:

2009 Fifth International Conference on Natural Computation  (Volume:4 )

Date of Conference:

14-16 Aug. 2009