Asian Journal of Research in Social Sciences and Humanities
Year : 2016, Volume : 6, Issue : 5
First page : ( 836) Last page : ( 844)
Online ISSN : 2249-7315.
Article DOI : 10.5958/2249-7315.2016.00155.6

Improving Classification Performance with PCA-CFS and PCA-SA Feature Selector

Subashini S.*, Dr. Balamurugan S. Appavu Alias**

*Department of Computer Science and Engineering, Fatima Michael College of Engineering & Technology, Madurai, India

**Department of Information Technology, K.L.N. College of Information Technology, Madurai, India

Online published on 3 May, 2016.

Abstract

Data mining techniques are increasingly being used in Health care analytics to understand the probability of predicted outcomes. High dimensional data the identification of most relevant features that will best determine the target is a challenging task. Feature selection is aims to remove non-informative features from data mining models. This work is focused on improving the detection performance of classification algorithms by the use of Principal Component Analysis (PCA) based feature extraction and Correlation-based Feature Selection (CFS) and Shapley Values Analysis (SA) based feature ranking approach. The performance of projected hybrid method was evaluated through experiments on six medical datasets with various classifiers. The examined result projected the proposed hybrid feature selectors using the PCA-CFS and PCA-SA improves the detection performance of individual classification algorithms over the other methods in the literature.

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Keywords

Feature Selection, Principle Component Analysis, Correlation-based Feature Selection, Shapley Values Analysis, Medical datasets.

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