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Reseach Article

Cost Effective Approach on Feature Selection using Genetic Algorithms and LS-SVM Classifier

Published on None 2010 by E.P.Ephzibah
Evolutionary Computation for Optimization Techniques
Foundation of Computer Science USA
ECOT - Number 1
None 2010
Authors: E.P.Ephzibah
9c3e1b86-f6bb-4bb1-9e01-8bca128e1e5a

E.P.Ephzibah . Cost Effective Approach on Feature Selection using Genetic Algorithms and LS-SVM Classifier. Evolutionary Computation for Optimization Techniques. ECOT, 1 (None 2010), 16-20.

@article{
author = { E.P.Ephzibah },
title = { Cost Effective Approach on Feature Selection using Genetic Algorithms and LS-SVM Classifier },
journal = { Evolutionary Computation for Optimization Techniques },
issue_date = { None 2010 },
volume = { ECOT },
number = { 1 },
month = { None },
year = { 2010 },
issn = 0975-8887,
pages = { 16-20 },
numpages = 5,
url = { /specialissues/ecot/number1/1532-135/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Evolutionary Computation for Optimization Techniques
%A E.P.Ephzibah
%T Cost Effective Approach on Feature Selection using Genetic Algorithms and LS-SVM Classifier
%J Evolutionary Computation for Optimization Techniques
%@ 0975-8887
%V ECOT
%N 1
%P 16-20
%D 2010
%I International Journal of Computer Applications
Abstract

This work focuses on the problem of diagnosing the disease in the earlier stage by applying a selection technique based on genetic algorithm and least square support vector machines. The implementation of the technique analyses the accuracy of the classifier as well as the cost effectiveness in the implementation. This technique will help us to diagnose the disease with a limited number of tests that could be performed with minimal amount. We use evolutionary computation which is a subfield of artificial intelligence or computational intelligence that involves combinatorial optimization problems. Evolutionary computation uses iterative progress, such as growth or development in a population. This population is then selected in a guided random search using parallel processing to achieve the desired end. Such processes are often inspired by biological mechanisms of evolution. The obtained results using the genetic algorithms approach show that the proposed method is able to find an appropriate feature subset and SVM classifier achieves better results than other methods.

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Index Terms

Computer Science
Information Sciences

Keywords

Feature selection Genetic Algorithm Simulated Annealing Least Square Support Vector Machines classification