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Article:Feature Subset Selection using Cascaded GA & CFS: A Filter Approach in Supervised Learning

by Asha Gowda Karegowda, M.A.Jayaram, A.S .Manjunath
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 23 - Number 2
Year of Publication: 2011
Authors: Asha Gowda Karegowda, M.A.Jayaram, A.S .Manjunath
10.5120/2865-3711

Asha Gowda Karegowda, M.A.Jayaram, A.S .Manjunath . Article:Feature Subset Selection using Cascaded GA & CFS: A Filter Approach in Supervised Learning. International Journal of Computer Applications. 23, 2 ( June 2011), 1-10. DOI=10.5120/2865-3711

@article{ 10.5120/2865-3711,
author = { Asha Gowda Karegowda, M.A.Jayaram, A.S .Manjunath },
title = { Article:Feature Subset Selection using Cascaded GA & CFS: A Filter Approach in Supervised Learning },
journal = { International Journal of Computer Applications },
issue_date = { June 2011 },
volume = { 23 },
number = { 2 },
month = { June },
year = { 2011 },
issn = { 0975-8887 },
pages = { 1-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume23/number2/2865-3711/ },
doi = { 10.5120/2865-3711 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:09:06.749684+05:30
%A Asha Gowda Karegowda
%A M.A.Jayaram
%A A.S .Manjunath
%T Article:Feature Subset Selection using Cascaded GA & CFS: A Filter Approach in Supervised Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 23
%N 2
%P 1-10
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Medical data mining has enormous potential for exploring the hidden patterns in the data sets of the medical domain. These patterns can be utilized by the physicians to improve clinical diagnosis. Feature subset selection is one of data preprocessing step, which is of immense importance in the field of data mining. As a part of feature subset selection step of data preprocessing, a filter approach with genetic algorithm (GA) and Correlation based feature selection has been used in a cascaded fashion. GA rendered global search of attributes with fitness evaluation effected by CFS. Experimental results signify that the feature subset recognized by the proposed filter GA+CFS, when given as input to five classifiers, namely decision tree, Naïve Bayes, Bayesian, Radial basis function and k-nearest neighbor classifiers showed enhanced classification accuracy. Experiments have been carried out on four medical data sets publicly available at UCI.

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Index Terms

Computer Science
Information Sciences

Keywords

Feature selection filters Genetic Algorithm Correlation based feature selection Decision tree Naïve Bayes Bayesian Classifier Radial Basis Function K-Nearest Neighbor