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Comparative Study among Data Reduction Techniques over Classification Accuracy

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International Journal of Computer Applications
© 2015 by IJCA Journal
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 El M Bakry and Ahmed A Saleh. Article: Comparative Study among Data Reduction Techniques over Classification Accuracy. International Journal of Computer Applications 122(2):9-15, July 2015. Full text available. BibTeX

@article{key:article,
	author = {Ibrahim M. El-hasnony and Hazem M. El Bakry and Ahmed A. Saleh},
	title = {Article: Comparative Study among Data Reduction Techniques over Classification Accuracy},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {122},
	number = {2},
	pages = {9-15},
	month = {July},
	note = {Full text available}
}

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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