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      A Hybrid Algorithm Based on Binary Chemical Reaction Optimization and Tabu Search for Feature Selection of High-Dimensional Biomedical Data

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          Abstract

          In recent years, there have been rapid developments in various bioinformatics technologies, which have led to the accumulation of a large amount of biomedical data. The biomedical data can be analyzed to enhance assessment of at-risk patients and improve disease diagnosis, treatment, and prevention. However, these datasets usually have many features, which contain many irrelevant or redundant information. Feature selection is a solution that involves finding the optimal subset, which is known to be an NP problem because of the large search space. Considering this, a new feature selection approach based on Binary Chemical Reaction Optimization algorithm (BCRO) and k-Nearest Neighbors (KNN) classifier is presented in this paper. Tabu search is integrated with CRO framework to enhance local search capacity. KNN is adopted to evaluate the quality of selected candidate subset. The results for an experiment conducted on nine standard medical datasets demonstrate that the proposed approach outperforms other state-of-the-art methods.

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          Author and article information

          Journal
          TST
          Tsinghua Science and Technology
          Tsinghua University Press (Xueyan Building, Tsinghua University, Beijing 100084, China )
          1007-0214
          05 December 2018
          : 23
          : 6
          : 733-743
          Affiliations
          ∙ Chaokun Yan and Jingjing Ma are with the School of Computer and Information Engineering, Henan University, Kaifeng 475000, China. E-mail: ckyan@ 123456henu.edu.cn ; majingjing4729@ 123456163.com .
          ∙ Huimin Luo and Jianxin Wang are with the School of Information Science and Engineering, Central South University, Changsha 410083, China. E-mail: luohuimin@ 123456csu.edu.cn ;
          Author notes
          * To whom correspondence should be addressed. E-mail: jxwang@ 123456mail.csu.edu.cn .

          Chaokun Yan received the PhD degree from Central South University in 2013. He is an associate professor in School of Computer and Information Engineering, Henan University, Kaifeng, China. His research interests include data mining and bioinformatics.

          Jianxin Wang received the BEng and MEng degrees from Central South University, China, in 1992 and 1996, respectively, and the PhD degree in computer science from Central South University, China, in 2001. He is the vice dean and a professor in School of Information Science and Engineering, Central South University, Changsha, China. His current research interests include algorithm analysis and optimization, parameterized algorithm, bioinformatics, and computer network. He is a senior member of the IEEE.

          Huimin Luo is working toward the PhD degree in the School of Information Science and Engineering, Central South University. Her current research interests include bioinformatics and systems biology.

          Jingjing Ma received the bachelor degree from Henan Normal University in 2016. She is now a master student in the School of Computer and Information Engineering, Henan University. Her research interests are bioinformatics, data mining, and deep learning.

          Article
          1007-0214-23-6-733
          10.26599/TST.2018.9010101

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