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SVM Multi-Classification Optimization Research based on Multi-Chromosome Genetic Algorithm

Volume 14, Number 4, April 2018, pp. 631-638
DOI: 10.23940/ijpe.18.04.p5.631638

Ren Qian, Yun Wu, Xun Duan, Guangqian Kong, and Huiyun Long

College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China

(Submitted on January 3, 2018; Revised on February 14, 2018; Accepted on March 25, 2018)


Regarding SVM multi-classification problem, optimizing the parameters of SVM has become the key problem to improve the performance of the SVM multi-classification algorithm. In order to solve this problem, multi-chromosome genetic algorithm is proposed in this paper and used to optimize these parameters. In the SVM multi-classification decision tree, the algorithm constructs a chromosome for SVM parameter of each node and improves the corresponding rules of crossover and mutation in the genetic algorithm. The improved genetic algorithm optimizes the parameters of SVM in all nodes in the SVM multi-classification decision tree. The experimental results show that the SVM multi-classification decision tree algorithm using the multi-chromosome genetic algorithm has higher classification quality, compared with the traditional multi-SVM multi-classification algorithm.


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