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  Volume 1, Number 2, December 2020, pages 131-153
DOI: https://doi.org/10.12989/mca.2020.1.2.131
 
open access

An optimal structure for ensemble feature selection
Amirreza Rouhi and Hossein Nezamabadi-pour

 
Abstract
    Today, Gene selection in microarray data is one of the most challenging subjects in the fields of medicine and machine learning. Due to the large number of features and small number of samples in microarray datasets, choosing the desirable genes in these data is a difficult task. Among several methods which have been proposed for gene (feature) selection, ensemble and hybrid methods have attracted more attentions. The purpose of this paper is to find an optimal structure for hybrid-ensemble gene selection method that, by selecting the least number of the genes, yields the desired classification accuracy. For this purpose, the genetic algorithm is used as one of the most popular evolutionary optimization methods to accomplish an optimal hybrid-ensemble feature selection method. The performance of the proposed method is widely tested on 18 microarray datasets, and it is compared to those of the 10 well-known gene selection methods in terms of classification error rates and Gmean. Experimental results demonstrate that the obtained optimal method is considerably superior to the other competing methods over different evaluation methods and datasets.
 
Key Words
    gene selection; high-dimensional data; hybrid methods; metaheuristic; filter methods; ensemble methods
 
Address
Amirreza Rouhi:Intelligent Data Processing Laboratory (IDPL), Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran/ Department of Electronics and Information, Politecnico di Milano, Italy
Hossein Nezamabadi-pour:Intelligent Data Processing Laboratory (IDPL), Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
 

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