By Topic

DNA Microarray Data Analysis: Effective Feature Selection for Accurate Cancer Classification

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

4 Author(s)
Jagdish C. Patra ; School of Computer Engineering, Nanyang Technological University, Singapore. E.mail: ; Goh P. Lim ; Pramod K. Meher ; Ee Luang Ang

Accurate classification of DNA microarray data is vital for cancer diagnosis and treatment. For greater accuracy, a preferable strategy is to make a decision based on the result of a single classifier that is trained with various aspects of data space. It is a difficult task to create an optimal classifier for DNA analysis that deals with only a few samples with large number of features. Usually, different feature sets are provided for classifiers to learn. If the feature sets provide similar information, the classifiers trained from them cannot improve the performance because they will make the same error and there is no possibility of compensation. In this paper, we adopt correlation analysis of feature selection methods as a guideline for selection of features for classifiers to learn. We use a negative correlation method for generation of feature sets those are mutually exclusive. Each classifier is learned from different features sets based on correlation analysis to classify cancer precisely. In this way, we evaluated the performance with two benchmark datasets. Experimental results show that classifiers, which have learned from different feature sets that are negatively correlated with each other, produce the best recognition rates on the two benchmark datasets.

Published in:

2007 International Joint Conference on Neural Networks

Date of Conference:

12-17 Aug. 2007