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Comparative Study among Data Reduction Techniques over Classification Accuracy

by Ibrahim M. El-hasnony, Hazem M. El Bakry, Ahmed A. Saleh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 122 - Number 2
Year of Publication: 2015
Authors: Ibrahim M. El-hasnony, Hazem M. El Bakry, Ahmed A. Saleh
10.5120/21671-4752

Ibrahim M. El-hasnony, Hazem M. El Bakry, Ahmed A. Saleh . Comparative Study among Data Reduction Techniques over Classification Accuracy. International Journal of Computer Applications. 122, 2 ( July 2015), 9-15. DOI=10.5120/21671-4752

@article{ 10.5120/21671-4752,
author = { Ibrahim M. El-hasnony, Hazem M. El Bakry, Ahmed A. Saleh },
title = { Comparative Study among Data Reduction Techniques over Classification Accuracy },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 122 },
number = { 2 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 9-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume122/number2/21671-4752/ },
doi = { 10.5120/21671-4752 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:09:30.739828+05:30
%A Ibrahim M. El-hasnony
%A Hazem M. El Bakry
%A Ahmed A. Saleh
%T Comparative Study among Data Reduction Techniques over Classification Accuracy
%J International Journal of Computer Applications
%@ 0975-8887
%V 122
%N 2
%P 9-15
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nowadays, Healthcare is one of the most critical issues that need efficient and effective analysis. Data mining provides many techniques and tools that help in getting a good analysis for healthcare data. Data classification is a form of data analysis for deducting models. Mining on a reduced version of data or a lower number of attributes increases the efficiency of system providing almost the same results. In this paper, a comparative study between different data reduction techniques is introduced. Such comparison is tested against classification algorithms accuracy. The results showed that fuzzy rough feature selection outperforms rough set attribute selection, gain ratio, correlation feature selection and principal components analysis.

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Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy rough feature selection rough set attribute reduction principal component analysis correlation feature selection gain ratio

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