Improving nearest neighbor classification by elimination of noisy irrelevant features

Authors:
M. Javad Zomorodian;Ali Adeli;Mehrnoosh Sinaee;Sattar Hashemi
Affiliations:
Institute of Computer Science, Shiraz Bahonar Technical College, Shiraz, Iran and Department of Computer Science and Engineering, Shiraz University, Shiraz, Iran;Department of Computer Science and Engineering, Shiraz University, Shiraz, Iran;Department of Computer Science and Engineering, Shiraz University, Shiraz, Iran;Department of Computer Science and Engineering, Shiraz University, Shiraz, Iran
Venue:
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Year:
2012

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Abstract

This paper introduces the use of GA with a novel fitness function to eliminate noisy and irrelevant features. Fitness function of GA is based on the Area Under the receiver operating characteristics Curve (AUC). The aim of this feature selection is to improve the performance of k-NN algorithm. Experimental results show that the proposed method can substantially improve the classification performance of k-NN algorithm in comparison with the other classifiers (in the realm of feature selection) such as C4.5, SVM, and Relief. Furthermore,this method is able to eliminate the noisy irrelevant features from the synthetic data sets.