Wavelet Lp-Norm Support Vector Regression with Feature Selection
Ya-Fen Ye*,**, Yuan-Hai Shao*, and Chun-Na Li*
*Zhijiang College, Zhejiang University of Technology
182 Zhijiang Road, Hangzhou 310024, China
**College of Economics, Zhejiang University
Hangzhou 310027, China
This paper proposes wavelet Lp-norm support vector regression (Lp-WSVR) to solve feature selection and regression problems effectively. Unlike conventional support vector regression (SVR), linear Lp-WSVR ensures that useful features are selected based on theoretical analysis. By using the wavelet kernel,Lp-WSVR approaches any curve in quadratic continuous integral space that leads to improving regression performance. Results of experiments show the superiority of Lp-WSVR in both feature selection and regression performances. Applying Lp-WSVR to Chinese real estate prices shows that the most significant and powerful factor contributing to Chinese housing prices is monetary growth.
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