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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)

Abstract:

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.

 

References: 18

    1. S. Besbes, Z. Lachir, "Multi-class SVM for Stressed Speech Recognition," International Conference on Advanced Technologies for Signal and Image Processing, IEEE, pp. 782-787, 2016
    2. G. S. Cho, N. Gantulga, Y. W. Choi, "A Comparative Study on Multi-class SVM & Kernel Function for Land Cover Classification in a KOMPSAT-2 Image, "Ksce Journal of Civil Engineering, pp. 1-11, 2016
    3. H. Guo, W. Wang, "An Active Learning-based SVM Multi-class Classification model," Pattern Recognition, vol. 48, no. 5, pp. 1577-1597, 2015
    4. S. Kumar, S. Mishra, P. Khanna, "Precision Sugarcane Monitoring Using SVM Classifier," Procedia Computer Science, vol. 122, pp. 881-887, 2017
    5. K. C. Lin, T. Y. Liu, P. H. Chen, "Use Support Vector Machine (SVM) to Estimate Gas Concentration in Mixture Condition," International Conference on Applied System Innovation. IEEE, pp. 944-746, 2017
    6. P. Li, H. Meng, X. Wang, "A Feature Selection Method Based on the Sparse Multi-class SVM for Fingerprinting Localization," Vehicular Technology Conference, IEEE, pp. 1-5, 2014
    7. Q. Li, L. Chen, "Study on Multi-class Text Classification Based on Improved SVM," Practical Applications of Intelligent Systems, Springer Berlin Heidelberg, pp. 519-516, 2014
    8. A. S. Murugavel, S. Ramakrishnan, "Hierarchical Multi-class SVM with ELM Kernel for Epileptic EEG Signal Classification," Medical & Biological Engineering & Computing, vol. 54, no. 1, pp. 149-161, 2016
    9. J. Novakovic, A. Veljovic, S. S. Ilic, "Improving the Accuracy of SVM Algorithm in Classification Problems with PCA Method," Intelligent Information Technologies for Industry, pp. 66-73, 2018
    10. A. Reda, E. Fakharany, M. Hazman, "Early Prediction of Wheat Diseases Using SVM Multiclass," Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, pp. 257-269, 2018
    11. B. Sidaoui, K. Sadouni, "Binary Tree Multi-class SVM Based on OVA Approach and Variable Neighbourhood Search Algorithm," International Journal of Computer Applications in Technology, vol. 55, no. 3, pp. 183-189, 2017
    12. E. Saeedi, M. S. Hossain, Y. Kong, "Multi-class SVMs Analysis of Side-channel Information of Elliptic Curve Cryptosystem," International Symposium on PERFORMANCE Evaluation of Computer and Telecommunication Systems, IEEE, pp. 1-6, 2015
    13. M. Shaik, "Improved Normalization Approach for Iris Image Classification Using SVM," Advances in Electronics, Communication and Computing, pp. 139-145, 2018
    14. W. Wenjing, S. Xiaohua, "Classification of Images of Strip Surface Defects Based on the Improved SVM Algorithm," Revista de la Facultad de Ingenieria, vol. 32, nol. 1, pp. 244-254, 2017
    15. Y. Xue, P. Beausero, "Transfer Learning for One Class SVM Adaptation to Limited Data Distribution Change," Pattern Recognition Letters, pp. 100-108, 2017
    16. C. Yin, L. Shi, J. Wang, "Short Text Classification Technology Based on KNN+Hierarchy SVM," Advanced Multimedia and Ubiquitous Engineering, pp. 633-639, 2017
    17. J. Zhang, C. Zhao, F. Xu, "SVM-Based Sentiment Analysis Algorithm of Chinese Microblog Under Complex Sentence Pattern," International Conference in Communications, Signal Processing, and Systems, pp. 801-809, 2016
    18. Z. Zhao, X. Zhang, M. Han M, "Research on Multi-Faults Classification of Hoister Based on Improved LMD and Multi-class SVM," International Symposium on Computational Intelligence & Design, IEEE, pp. 573-576, 2015

       

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