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A Comprehensive Survey on various Feature Selection Methods to Categorize Text Documents

by B. S. Harish, M. B. Revanasiddappa
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 164 - Number 8
Year of Publication: 2017
Authors: B. S. Harish, M. B. Revanasiddappa
10.5120/ijca2017913711

B. S. Harish, M. B. Revanasiddappa . A Comprehensive Survey on various Feature Selection Methods to Categorize Text Documents. International Journal of Computer Applications. 164, 8 ( Apr 2017), 1-7. DOI=10.5120/ijca2017913711

@article{ 10.5120/ijca2017913711,
author = { B. S. Harish, M. B. Revanasiddappa },
title = { A Comprehensive Survey on various Feature Selection Methods to Categorize Text Documents },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2017 },
volume = { 164 },
number = { 8 },
month = { Apr },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume164/number8/27500-2017913711/ },
doi = { 10.5120/ijca2017913711 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:10:43.816940+05:30
%A B. S. Harish
%A M. B. Revanasiddappa
%T A Comprehensive Survey on various Feature Selection Methods to Categorize Text Documents
%J International Journal of Computer Applications
%@ 0975-8887
%V 164
%N 8
%P 1-7
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Feature selection is one of the well known solution to high dimensionality problem of text categorization. In text categorization, selection of good features (terms) plays a very important role. Feature selection is a strategy that can be used to improve categorization accuracy, effectiveness and computational efficiency. This paper presents an empirical study of most widely used feature selection methods viz. Term Frequency-Inverse Document Frequency (tf idf ), Information Gain (IG), Mutual Information(MI), CHI-Square ( 2), Ambiguity Measure (AM), Term Strength (TS), Term Frequency-Relevance Frequency (tf rf ) and Symbolic Feature Selection (SFS) with five different classifiers (Nave Bayes, KNearest Neighbor, Centroid Based Classifier, Support Vector Machine and Symbolic Classifier). Experimentations are carried out on standard bench mark datasets like Reuters-21578, 20-Newsgroups and 4 University dataset.

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Index Terms

Computer Science
Information Sciences

Keywords

High Dimensionality Feature Selection Classifiers Text Categorization

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