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Effective Approach for Classification of Nominal Data

IJCA Proceedings on National Conference on Advances in Computing
© 2015 by IJCA Journal
NCAC 2015 - Number 6
Year of Publication: 2015
Ketan Sanjay Desale
Balaji Govind Shelale
Sushant Navsare
Dipak Bodade
Krishnkumar. K. Khandelwal

Ketan Sanjay Desale, Balaji Govind Shelale, Sushant Navsare, Dipak Bodade and Krishnkumar.k.khandelwal. Article: Effective Approach for Classification of Nominal Data. IJCA Proceedings on National Conference on Advances in Computing NCAC 2015(6):28-32, December 2015. Full text available. BibTeX

	author = {Ketan Sanjay Desale and Balaji Govind Shelale and Sushant Navsare and Dipak Bodade and Krishnkumar.k.khandelwal},
	title = {Article: Effective Approach for Classification of Nominal Data},
	journal = {IJCA Proceedings on National Conference on Advances in Computing},
	year = {2015},
	volume = {NCAC 2015},
	number = {6},
	pages = {28-32},
	month = {December},
	note = {Full text available}


In today's era, network security has become very important and a severe issue in information and data security. The data present over the network is profoundly confidential. In order to perpetuate that data from malicious users a stable security framework is required. Intrusion detection system (IDS) is intended to detect illegitimate access to a computer or network systems. With advancement in technology by WWW, IDS can be the solution to stand guard the systems over the network. Over the time data mining techniques are used to develop efficient IDS. Here,a new approach is introduced by assembling data mining techniques such as data preprocessing, feature selection and classification for helping IDS to attain a higher detection rate. The proposed techniques have three building blocks: data preprocessing techniques are used to produce final subsets. Then, based on collected training subsets various feature selection methods are applied to remove irrelevant & redundant features. The efficiency of above ensemble is checked by applying it to the different classifiers such as naive bayes, J48. By experimental results, for credit-gdataset, using discretize or normalize filter with CAE accuracy of both classifiers i. e. naive bayes & J48 is increased. For vote dataset, using discretize or normalize filter with CFS accuracy of the naive bayes classifier increased.


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