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Reseach Article

Effective Feature Selection Approach using Genetic Algorithm for Numerical Data

Published on December 2015 by Ketan Sanjay Desale, Balaji Mane, Prashant Berkile, Sushant Shivale
National Conference on Advances in Computing
Foundation of Computer Science USA
NCAC2015 - Number 6
December 2015
Authors: Ketan Sanjay Desale, Balaji Mane, Prashant Berkile, Sushant Shivale
30036af1-e219-45b0-bda1-daa053c1e88e

Ketan Sanjay Desale, Balaji Mane, Prashant Berkile, Sushant Shivale . Effective Feature Selection Approach using Genetic Algorithm for Numerical Data. National Conference on Advances in Computing. NCAC2015, 6 (December 2015), 24-27.

@article{
author = { Ketan Sanjay Desale, Balaji Mane, Prashant Berkile, Sushant Shivale },
title = { Effective Feature Selection Approach using Genetic Algorithm for Numerical Data },
journal = { National Conference on Advances in Computing },
issue_date = { December 2015 },
volume = { NCAC2015 },
number = { 6 },
month = { December },
year = { 2015 },
issn = 0975-8887,
pages = { 24-27 },
numpages = 4,
url = { /proceedings/ncac2015/number6/23398-5069/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advances in Computing
%A Ketan Sanjay Desale
%A Balaji Mane
%A Prashant Berkile
%A Sushant Shivale
%T Effective Feature Selection Approach using Genetic Algorithm for Numerical Data
%J National Conference on Advances in Computing
%@ 0975-8887
%V NCAC2015
%N 6
%P 24-27
%D 2015
%I International Journal of Computer Applications
Abstract

Data mining methods are used to handle the problems of dynamic huge data set. To build a classification model, time complexity of calculated result can be scale back by selecting only useful features. A feature selection technique is used to select only useful features from available features. An intersection principle based feature selection approach is Used. Genetic algorithm is used as a search method and it select only the features which are appears frequently in datasets. Then results were tested for different datasets having different type of data using Naive Bayes & J48 classifiers. The result analysis shows that Naive Bayes classifier gives better result than J48 classifier, with the substantial growth in accuracy, minimum time and minimum number of features. In this paper correlation feature selection is used with Genetic Algorithm for feature selection.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Dimensionality Reduction Feature Selection Genetic Algorithm (ga) Naïve Bayes J48