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Wavelet twin support vector machine

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Abstract

Twin support vector machine (TWSVM) is a research hot spot in the field of machine learning in recent years. Although its performance is better than traditional support vector machine (SVM), the kernel selection problem still affects the performance of TWSVM directly. Wavelet analysis has the characteristics of multivariate interpolation and sparse change, and it is suitable for the analysis of local signals and the detection of transient signals. The wavelet kernel function based on wavelet analysis can approximate any nonlinear functions. Based on the wavelet kernel features and the kernel function selection problem, wavelet twin support vector machine (WTWSVM) is proposed by this paper. It introduces the wavelet kernel function into TWSVM to make the combination of wavelet analysis techniques and TWSVM come true. The experimental results indicate that WTWSVM is feasible, and it improves the classification accuracy and generalization ability of TWSVM significantly.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61379101) and the National Key Basic Research Program of China (No. 2013CB329502).

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Shifei Ding & Fulin Wu

  2. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Shifei Ding, Fulin Wu & Zhongzhi Shi

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  1. Shifei Ding
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  2. Fulin Wu
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  3. Zhongzhi Shi
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Correspondence to Shifei Ding.

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Ding, S., Wu, F. & Shi, Z. Wavelet twin support vector machine. Neural Comput & Applic 25, 1241–1247 (2014). https://doi.org/10.1007/s00521-014-1596-y

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  • DOI: https://doi.org/10.1007/s00521-014-1596-y

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