Simultaneous feature selection and feature weighting using Hybrid Tabu Search/K-nearest neighbor classifier |
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| Affiliation: | 1. School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, Jiangsu, 212013, PR China;2. College of Electrical and Information Engineering, North Minzu University, Yinchuan, Ningxia, 750021, PR China;3. School of Big Data and Computer Science, Guizhou Normal University, Guiyang, Guizhou, 550025, PR China;4. School of Information Science and Technology, Huizhou University, Huizhou, Guangdong, 516007, PR China;5. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, PR China;1. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, 250022, PR China;2. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150002, PR China;3. Department of Compute Science, University of Otago, Dunedin, New Zealand;1. Faculty of Computers and Informatics, Department of Operations Research, Zagazig University, Egypt;2. Computer Science Department, Faculty of Computers and Informatics, Zagazig University, Egypt;3. University of Fortaleza, Fortaleza, Ceará, Brazil;4. Torrens University Australia, 90 Bowen Terrace, Fortitude Valley, Queensland 4006, Australia |
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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. |
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