By Topic

Maximizing Correlation for Supervised 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
$31 $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

2 Author(s)
Mahata, K. ; Univ. of Newcastle, Callaghan ; Mahata, P.

In this paper, we develop a novel feature selection and classification approach using the correlation maximization paradigm. This approach is particularly interesting when the number of features is very large in comparison to the number of samples, as in the datasets arising in the bioinformatics applications. We illustrate our method by showing 100 genetic markers which act together in separating ovarian endometroid tumors from other ovarian epithelial tumors. Notice that in the previous works, there was no single marker gene found for this purpose.

Published in:

Digital Signal Processing, 2007 15th International Conference on

Date of Conference:

1-4 July 2007