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Feature selection through feature clustering for microarray gene expression data

conference contribution
posted on 06.12.2017, 00:00 authored by Choudhury WahidChoudhury Wahid, A B M Shawkat Ali, Kevin TickleKevin Tickle
A subset of features from a large data set is sufficient to improve the classifier performance in the user end. In this paper we have presented a novel approach for feature selection based on feature clustering using the well known k-means philosophy for the high dimensional gene expression data. This novel cluster based feature selection approach is applied on micro array gene expression data classification, exclusively in various cancer patient identification problems. We have used the popular box and whisker plot to represent our experimental performance in terms of accuracy and computational time. The experimental outcome clearly shows the suitability of our algorithm in the micro array gene expression domain.

History

Parent Title

Proceedings of the IASTED International Conference Artificial Intelligence and Applications (AIA 2011).

Start Page

115

End Page

121

Number of Pages

7

Start Date

01/01/2011

Location

Innsbruck, Austria

Publisher

IASTED

Place of Publication

Austria

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Centre for Intelligent and Networked Systems (CINS); Institute for Resource Industries and Sustainability (IRIS); International Conference Artificial Intelligence and Applications;

Era Eligible

Yes

Name of Conference

International Association for Science and Technology for Development. International Conference Artificial Intelligence and Applications