ZHOU Jiarui, ZHU Zexuan, JI Zhen, “A Memetic Algorithm Based Feature Weighting for Metabolomics Data Classification,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 706-711, 2014,
Citation: ZHOU Jiarui, ZHU Zexuan, JI Zhen, “A Memetic Algorithm Based Feature Weighting for Metabolomics Data Classification,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 706-711, 2014,

A Memetic Algorithm Based Feature Weighting for Metabolomics Data Classification

Funds:  This work was supported in part by the National Natural Science Foundation of China Joint Fund with Guangdong, under Key Project (No.U1201256), the National Natural Science Foundation of China (No.61171125, No.61001185), in part by the NSFC-RS joint project (No.61211130120), in part by the Fok Ying-Tung Education Foundation, Guangdong Natural Science Foundation (No.S2012010009545), in part by Scientific Research Foundation for the Returned Overseas Chinese Scholars, Ministry of Education of China, in part by Science Foundation of Shenzhen City (No.JC201105170650A, No.KQC201108300045A), and in part by the Shenzhen City Foundation for Distinguished Young Scientists.
  • Received Date: 2013-03-01
  • Rev Recd Date: 2013-06-01
  • Publish Date: 2014-10-05
  • A metaheuristic chain based memetic algorithm namely MCMA is proposed for the classification of metabolomics data. MCMA regards both global evolution and local search as equivalent elemental metaheuristics, and searches with a chain of metaheuristics performed alternatively on the target problem. A hidden Markov model based scheduling mechanism is employed in MCMA for the selection of metaheuristics. By using MCMA for optimizing the linkage weight vector, a feature weighting algorithm for metabolomics data is formed to identify relevant metabolite features and reveal their exact relationships with the given physiological states. An extreme learning machine based classifier is utilized in predicting the physiological states according to the weighted metabolite features. Experimental results on real metabolomics data of clinical liver transplantation demonstrate that the proposed feature weighting and classification method obtains better performance than the other compared algorithms.
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