Title: A hybrid-ensemble based framework for microarray data gene selection

Authors: Amirreza Rouhi; Hossein Nezamabadi-pour

Addresses: Intelligent Data Processing Laboratory (IDPL), Department of Electrical Engineering, Shahid Bahonar University of Kerman, P.O. Box 76169-133, Kerman, Iran; Mahani Mathematical Research Center, Shahid Bahonar University of Kerman, Kerman, Iran ' Intelligent Data Processing Laboratory (IDPL), Department of Electrical Engineering, Shahid Bahonar University of Kerman, P.O. Box 76169-133, Kerman, Iran; Mahani Mathematical Research Center, Shahid Bahonar University of Kerman, Kerman, Iran

Abstract: With the advent and propagation of high-dimensional microarray data, the process of gene selection has now become far more difficult and time-consuming, and classic feature selection methods are quickly becoming obsolete. Dealing with high-dimensional biomedical data is associated with problems such as the curse of dimensionality and increased presence of redundant and irrelevant genes, which all lead to significant rise in classification error. This paper provides a framework for combined use of ensemble and hybrid methods for gene selection in high-dimensional data with the aim of increasing classification accuracy and reducing dimensionality. The proposed method is benchmarked using several microarray datasets. The comparison results with those of latest ensemble feature selection methods confirm the good performance of the proposed approach.

Keywords: gene selection; feature selection; microarray data; hybrid methods; metaheuristic; ensemble methods.

DOI: 10.1504/IJDMB.2017.090987

International Journal of Data Mining and Bioinformatics, 2017 Vol.19 No.3, pp.221 - 242

Received: 25 Apr 2017
Accepted: 26 Nov 2017

Published online: 05 Apr 2018 *

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