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Reseach Article

A Hybrid Feature Selection Method to Improve Performance of a Group of Classification Algorithms

by Mehdi Naseriparsa, Amir-masoud Bidgoli, Touraj Varaee
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 17
Year of Publication: 2013
Authors: Mehdi Naseriparsa, Amir-masoud Bidgoli, Touraj Varaee
10.5120/12065-8172

Mehdi Naseriparsa, Amir-masoud Bidgoli, Touraj Varaee . A Hybrid Feature Selection Method to Improve Performance of a Group of Classification Algorithms. International Journal of Computer Applications. 69, 17 ( May 2013), 28-35. DOI=10.5120/12065-8172

@article{ 10.5120/12065-8172,
author = { Mehdi Naseriparsa, Amir-masoud Bidgoli, Touraj Varaee },
title = { A Hybrid Feature Selection Method to Improve Performance of a Group of Classification Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 17 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 28-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number17/12065-8172/ },
doi = { 10.5120/12065-8172 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:30:32.555241+05:30
%A Mehdi Naseriparsa
%A Amir-masoud Bidgoli
%A Touraj Varaee
%T A Hybrid Feature Selection Method to Improve Performance of a Group of Classification Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 17
%P 28-35
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper a hybrid feature selection method is proposed which takes advantages of wrapper subset evaluation with a lower cost and improves the performance of a group of classifiers. The method uses combination of sample domain filtering and resampling to refine the sample domain and two feature subset evaluation methods to select reliable features. This method utilizes both feature space and sample domain in two phases. The first phase filters and resamples the sample domain and the second phase adopts a hybrid procedure by information gain, wrapper subset evaluation and genetic search to find the optimal feature space. Experiments carried out on different types of datasets from UCI Repository of Machine Learning databases and the results show a rise in the average performance of five classifiers (Naïve Bayes, Logistic, Multilayer Perceptron, Best First Decision Tree and JRIP) simultaneously and the classification error for these classifiers decreases considerably. The experiments also show that this method outperforms other feature selection methods with a lower cost.

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Index Terms

Computer Science
Information Sciences

Keywords

Feature Selection Resampling Information Gain Wrapper Subset Evaluation