Simultaneous feature selection and feature weighting using Hybrid Tabu Search/K-nearest neighbor classifier

Authors:
Muhammad Atif Tahir;Ahmed Bouridane;Fatih Kurugollu
Affiliations:
School of Computer Science, University of the West of England, Bristol, BS16 1QY, United Kingdom;School of Computer Science, The Queen's University of Belfast, Belfast BT7 1NN, Northern Ireland, United Kingdom;School of Computer Science, The Queen's University of Belfast, Belfast BT7 1NN, Northern Ireland, United Kingdom
Venue:
Pattern Recognition Letters
Year:
2007

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Abstract

Feature selection and feature weighting are useful techniques for improving the classification accuracy of K-nearest-neighbor (K-NN) 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 pattern classes. In this paper, a novel hybrid approach is proposed for simultaneous feature selection and feature weighting of K-NN rule based on Tabu Search (TS) heuristic. The proposed TS heuristic in combination with K-NN classifier is compared with several classifiers on various available data sets. The results have indicated a significant improvement in the performance in classification accuracy. The proposed TS heuristic is also compared with various feature selection algorithms. Experiments performed revealed that the proposed hybrid TS heuristic is superior to both simple TS and sequential search algorithms. We also present results for the classification of prostate cancer using multispectral images, an important problem in biomedicine.