Simultaneous Feature Selection and Weighting for Nearest Neighbor Using Tabu Search

Abstract

Both feature selection and feature weighting techniques are useful for improving the classification accuracy of K-nearest-neighbor (KNN) rule. The term feature selection refers to algorithms that select the best subset of the input feature set. In feature weighting, each feature is multiplied by a weight value proportional to the ability of the feature to distinguish among pattern classes. In this paper, a tabu search based heuristic is proposed for simultaneous feature selection and feature weighting of KNN rule. The proposed heuristic in combination with KNN classifier is compared with several classifiers on various available datasets. Results have indicated a significant improvement of the performance in terms of maximizing classification accuracy.