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World Journal of Engineering
Research and Technology

An International Peer Reviewed Journal for Engineering Research and Technology

ISSN 2454-695X

Impact Factor : 5.924

ICV : 79.45

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Indexing

Abstract

STUDY ON FEATURE SELECTION AND FEATURE EXTRACTION TECHNIQUES IN DATA MINING

*Wasim Akram and Prof. S. R. Yadav

ABSTRACT

Dimensionality reduction in data mining focuses on representing data with minimum number of dimensions such that its properties are not lost and hence reducing the underlying complexity in processing the data. Principal Component Analysis (PCA) is one of the prominent dimensionality reduction techniques widely used in network traffic analysis. In this paper, efficiency of PCA and SPCA has been emphasized for intrusion detection and its Reduction Ratio (RR) has been determined, ideal number of Principal Components needed for intrusion detection and the impact of noisy data on PCA. Feature selection and Feature Extraction are one of the methods used to reduce the dimensionality. Till now these methods were using separately so the resultant feature contains original or transformed data. An efficient algorithm for Feature Selection and Extraction using Feature Subset Technique (FSEFST) in High Dimensional Data has been proposed in order to select and extract the efficient features by using feature subset method where it will have both original and transformed data. The results prove that the suggested method is better as compared with the existing algorithm.

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