New Fuzzy Support Vector Machine Method Based on Entropy and Ant-Colony Optimization

Abstract:

Concerning the defect of fuzzy membership as a function of distance between the point and its class center in feature space for some current Fuzzy Support Vector Machines (FSVM), a new FSVM based on entropy and Genetic Algorithm (GA) named EGFSVM was proposed in this paper. Making use of evaluation of entropy and intelligence of GA, EGFSVM enhances the classification capability and makes clustering center more suitable and membership more accurate. Experimental results show EGFSVM has better precision and classification performance, especially to multi-class and large scale data.

• Periodical: Applied Mechanics and Materials (Volumes 380-384)
• Pages: 1580-1584
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.380-384.1580
• Online since: August 2013
• Authors: Hao Guang Chen, Xiao Xi Li, Da Xi Li
• Keywords: Clustering, Entropy, Fuzzy Support Vector Machine (FSVM), Genetic Algorithm (GA)

References

[1] CORTESC, VAPNIK V. Support vector networks[J]. Machine Learning, 1995, 20(3): 273-297.

[2] VAPNIKV. Estimation of dependence based on empirical data[M]. Berlin: Springer, (1982).

[3] GUYON I, MATIC N, VAPNIK V N. Discovering informative patterns and data cleaning[M]. Advances in Knowledge Discovery and Data Mining. Menlo Park, CA, USA: American Association for Artificial Intelligence, 1996: 181-203.

[4] LIN Chunfu, WANG Shengde. Fuzzy support vector machines[J]. IEEE Transactions on Neural Networks, 2002, 13(2): 464-471.

DOI: 10.1109/72.991432

[5] INOUE T, ABE S. Fuzzy support vector machines for pattern classification[C]. Proceedings of IJCNN. New York, USA: IEEE Press, 2001: 1449-1454.

[6] HUANG H P, LIU Y H. Fuzzy support vector machine for pattern recognition and data mining[J]. International Journal of Fuzzy Systems, 2002, 4 (3): 826-835.

[7] LIN C F, WANG S D. Fuzzy support vectormachine[J]. IEEE Transactions on Neural Networks, 2002, 13(2): 464-471.

[8] JIANG XIUFENG, YI ZHANG, LV JIAN CHENG. Fuzzy SVM with a new fuzzy membership function[J]. Neural Computing and Applications, 2006, 15(3/4): 268-276.

DOI: 10.1007/s00521-006-0028-z

[9] FULIN C, WANG SHENGDE. Fuzzy support vector machines with automatic membership setting[C]. Studies in Fuzziness and Soft Computing. [S. l]: Springer, 2005: 233-254.

DOI: 10.1007/10984697_11

[10] Teh Hsiao Wei, Fan Hua, Wu Liang Liang. Entropy coefficient to the theoretical modeling analyzes and parallel implementation[J]. Mini-Micro Systems, 2007, 28(10): 1184-1187. (in chinese).

[11] Yang Shuyin. Pattern Recognition and Intelligent Computing (2nd edition)[M] Beijing: Electronic Industry Press, 2008. (in chinese).

[12] Ye Ning, Wang Di, Dou Lijun. Information entropy and support vector relationship[J]. Guangxi Normal University (Natural Science), 2006, 24 (4): (in chinese).