Analysis of Feature Selection Techniques: A Data Mining Approach

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IJCA Proceedings on International Conference on Advances in Emerging Technology
© 2016 by IJCA Journal
ICAET 2016 - Number 1
Year of Publication: 2016
Authors:
Sheena
Krishan Kumar
Gulshan Kumar

Sheena, Krishan Kumar and Gulshan Kumar. Article: Analysis of Feature Selection Techniques: A Data Mining Approach. IJCA Proceedings on International Conference on Advances in Emerging Technology ICAET 2016(1):17-21, September 2016. Full text available. BibTeX

@article{key:article,
	author = {Sheena and Krishan Kumar and Gulshan Kumar},
	title = {Article: Analysis of Feature Selection Techniques: A Data Mining Approach},
	journal = {IJCA Proceedings on International Conference on Advances in Emerging Technology},
	year = {2016},
	volume = {ICAET 2016},
	number = {1},
	pages = {17-21},
	month = {September},
	note = {Full text available}
}

Abstract

Feature Selection plays the very important role in Intrusion Detection System. One of the major challenge these days is dealing with large amount of data extracted from the network that needs to be analyzed. Feature Selection helps in selecting the minimum number of features from the number of features that need more computation time, large space, etc. This paper, analyzed different feature selection technique on the NSL-KDD dataset by using C45 classifier, compared these techniques by various performance metrics like classifier accuracy, number of features selected, a list of features selected, elapsed time.

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