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Feature Selection Algorithm for enhancing Modeling Efficiency

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Manal Mostafa, Mohamed Gamal
10.5120/ijca2017915279

Manal Mostafa and Mohamed Gamal. Feature Selection Algorithm for enhancing Modeling Efficiency. International Journal of Computer Applications 173(4):1-7, September 2017. BibTeX

@article{10.5120/ijca2017915279,
	author = {Manal Mostafa and Mohamed Gamal},
	title = {Feature Selection Algorithm for enhancing Modeling Efficiency},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2017},
	volume = {173},
	number = {4},
	month = {Sep},
	year = {2017},
	issn = {0975-8887},
	pages = {1-7},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume173/number4/28320-2017915279},
	doi = {10.5120/ijca2017915279},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

This paper presents High Probability Minimum Redundancy, HPMR, as a new algorithm for employing the most predictive features to contribute dimensionality reduction. The proposed algorithm is useful for finding new, optimal, and more informative features maintaining acceptable classification accuracy. A problem encountered in many large-scale information applications relevant to expert and intelligent systems such as pattern recognition, bioinformatics, social media content classification where data sets containing massive numbers of features. Implementing categorization on these huge, uneven, useless datasets with the overwhelming number of features is just a waste of time degrading the efficiency of classification algorithms and hence the results are not much accurate. HPMR controls the tradeoff between relevance and redundancy by selecting new feature subset that retains sufficient information to discriminate well among classes.

To emphasize the significance of HPMR, it has been relied upon to develop an intelligent system for Arabic sentiment analysis on social media. Additionally, the performance of such algorithm is quantitatively compared with other traditional dimensionality reduction techniques in terms of performance accuracy, dataset reduction percentage, training time. Experimental results prove that HPMR cannot only diminish the feature vector but also can significantly enhance the performance of the well-known classifiers.

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Keywords

Feature Selection, HPMR, Chi-squared, Social Media, Arabic, SA, SVM.