REVIEW ARTICLE

A Study on the Relevance of Feature Selection Methods in Microarray Data

The Open Bioinformatics Journal 31 July 2018 REVIEW ARTICLE DOI: 10.2174/1875036201811010117

Abstract

Background:

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.

Conclusion:

We summarize this study by highlighting pointers to recent trends and challenges of feature selection research and development in microarray data.

Keywords: Microarray data, Data mining, Data mining tasks, Feature selection, Search strategies, Soft computing technique, Non-soft computing technique.
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