Journal of Jilin University Science Edition ›› 2021, Vol. 59 ›› Issue (5): 1212-1218.

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A Novel Self-adaptive Multiple Kernel Learning Algorithm

NIE Lusong1, CHANG Fangyuan2, CHANG Xuezhi3, LIU Chang4, JIN Youwei5,6,7, LIU Guosheng5,6,7, FU Jiasheng8, HAN Xiaosong5,6,7   

  1. 1. Jilin University Communist Youth League Committee, Jilin University, Changchun 130012, China;
    2. College of Nursing, Jilin University, Changchun 130012, China;
    3. College of Biological and Agricultural Engineering, Jilin University,  Changchun 130022, China;
    4. College of Medical Information, Changchun University of Chinese Medicine,  Changchun 130117, China;
    5. Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry, Changchun 130012, China; 
    6. College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    7. College of Software, Jilin University, Changchun 130012, China;  8. CNPC Engineering Technology R&D Company Limited, Beijing 102206, China
  • Received:2021-01-25 Online:2021-09-26 Published:2021-09-26

Abstract: Aiming at the problem of the training data set with large samples, high dimension, and complex features, a self-adaptive multiple kernel learning algorithm was proposed by integrating support vector machine with ant colony optimization algorithm. The affinity propagation clustering algorithm was used to find the similar features adaptively, and then the parameters of the kernel function were selected adaptively by ant colony algorithm, so as to select the optimal kernel function quikly. Experimental results of five groups of UCI data sets show that the proposed algorithm has higher classification accuracy and F1 value than the traditional support vector machine, which verifies the effectiveness and feasibility of the proposed algorithm.

Key words: multiple kernel learning, support vector machine, ant colony algorithm, clustering algorithm

CLC Number: 

  • TP391.1