Paper Info
Title
A new optimal gene selection approach for cancer classification using enhanced Jaya-based forest optimization algorithm
Abstract
In microarray experiments, the sample size is considerably smaller than that of the feature size, thereby imposing the curse of dimensionality problem. To resolve this issue, evolutionary algorithms are often utilized. In this paper, a novel framework for feature selection and classification of the microarray data is presented. Initially, a statistical filter, namely ANOVA, is used to select the relevant genes (features) from the original set of genes. Then, an evolutionary wrapper-based approach utilizing the principles of enhanced Jaya (EJaya) algorithm and forest optimization algorithm (FOA) is proposed to find the optimal set of genes from the previously selected genes. The main objective of using EJaya is to tune the two important parameters, namely local seeding changes and global seeding changes of FOA. During the selection of the optimal set of genes, support vector machine is employed as a classifier to classify the microarray data. To perform a comprehensive experimental study, the proposed method is tested on both binary-class and multi-class microarray datasets. From the extensive result analysis, it has been observed that the proposed technique achieves better classification accuracy with considerably less number of features than that of the benchmark schemes.
Year
DOI
Venue
2020
10.1007/s00521-019-04355-x
NEURAL COMPUTING & APPLICATIONS
Keywords
DocType
Volume
Microarray,ANOVA,Jaya,Forest optimization algorithm (FOA)
Journal
32.0
Issue
ISSN
Citations 
SP12.0
0941-0643
2
PageRank 
References 
Authors
0.35
0
3
Name
Order
Citations
PageRank
Santos Kumar Baliarsingh1192.32
Swati Vipsita2184.05
Bodhisattva Dash320.35