Research Article
A Gene Expression Data Classification and Selection Method using Hybrid Meta-heuristic technique
@ARTICLE{10.4108/eai.13-7-2018.159917, author={Rachhpal Singh}, title={A Gene Expression Data Classification and Selection Method using Hybrid Meta-heuristic technique}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={7}, number={25}, publisher={EAI}, journal_a={SIS}, year={2019}, month={8}, keywords={Gene Expression, Genetic Algorithm, Particle Swarm Optimization, Feature Selection Classification}, doi={10.4108/eai.13-7-2018.159917} }- Rachhpal Singh
Year: 2019
A Gene Expression Data Classification and Selection Method using Hybrid Meta-heuristic technique
SIS
EAI
DOI: 10.4108/eai.13-7-2018.159917
Abstract
The gene expression data selection is an ill-posed problem. The features selection techniques are found to be an efficient way to evaluate the dimensions of huge gene expression data. This feature selection techniques guide the relevant gene selection. In this paper, a hybrid method (MPG) is proposed to get selection of gene expression by using Mutual information way with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). A simulation environment is developed, which reveals the decrease in gene expression data dimensions and also removes the duplication among the classified gene data sets significantly. The proposed approach suitable for gene data set analysis using different classifier techniques and show the higher efficiency and accuracy of proposed data sets as compared to traditional selection mechanisms.
Copyright © 2019 Rachhpal Singh, licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
