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Neural Computing and Applications
Published in: Neural Computing and Applications 12/2020

27-07-2019 | Original Article

A new optimal gene selection approach for cancer classification using enhanced Jaya-based forest optimization algorithm

Authors: Santos Kumar Baliarsingh, Swati Vipsita, Bodhisattva Dash

Published in: Neural Computing and Applications | Issue 12/2020

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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.
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Metadata
Title
A new optimal gene selection approach for cancer classification using enhanced Jaya-based forest optimization algorithm
Authors
Santos Kumar Baliarsingh
Swati Vipsita
Bodhisattva Dash
Publication date
27-07-2019
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 12/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04355-x

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