A Study on the Relevance of Feature Selection Methods in Microarray Data
Barnali Sahu1, *, Satchidananda Dehuri2, Alok Jagadev3
Identifiers and Pagination:Year: 2018
First Page: 117
Last Page: 139
Publisher Id: TOBIOIJ-11-117
Article History:Received Date: 24/5/2018
Revision Received Date: 21/6/2018
Acceptance Date: 22/6/2018
Electronic publication date: 31/7/2018
Collection year: 2018
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
This paper studies the relevance of feature selection algorithms in microarray data for effective analysis. With no loss of generality, we present a list of feature selection algorithms and propose a generic categorizing framework that systematically groups algorithms into categories. The generic categorizing framework is based on search strategies and evaluation criteria. Further, it provides guidelines for selecting feature selection algorithms in general and in specific to the context of this study. In the context of microarray data analysis, the feature selection algorithms are classified into soft and non-soft computing categories. Their performance analysis with respect to microarray data analysis has been presented.
We summarize this study by highlighting pointers to recent trends and challenges of feature selection research and development in microarray data.