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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Improving Nearest Neighbor Classification by Elimination of Noisy Irrelevant Features
verfasst von : M. Javad Zomorodian, Ali Adeli, Mehrnoosh Sinaee, Sattar Hashemi
Erschienen in: Intelligent Information and Database Systems
Verlag: Springer Berlin Heidelberg
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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.