We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Ensemble Possibilistic K-NN for Functional Clustering of Gene Expression Profiles in Human Cancers Challenge

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

3 Author(s)
Fadeev, A. ; CECS, Univ. of Louisville, Louisville, KY, USA ; Missaoui, O. ; Frigui, H.

This paper describes the Ensemble Possibilistic K-NN algorithm for classification of gene expression profiles into three major cancer categories. In fact, a modification of forward feature selection is proposed to identify relevant feature subsets allowing for multiple possibilistic K-nearest neighbors (pK-NNs) rule experts. First, individual features are ranked according to their performance on training data and subsets of features identified using greedy approach. Each subset has significantly lower dimensionality than the original feature vector. Second, each subset is associated with pK-NN expert and the final classification decision is based on combining results produced by all experts.

Published in:

Machine Learning and Applications, 2009. ICMLA '09. International Conference on

Date of Conference:

13-15 Dec. 2009