A weighted Lq adaptive least squares support vector machine classifiers - Robust and sparse approximation

Authors:
Jingli Liu;Jianping Li;Weixuan Xu;Yong Shi
Affiliations:
School of Management, University of Science and Technology of China, No. 96 Jinzhai Road, 230026, Hefei, Anhui, China and Institute of Policy and Management, Chinese Academy of Sciences, No. 55 Zh ...;Institute of Policy and Management, Chinese Academy of Sciences, No. 55 Zhongguancun East Road, Beijing 100190, China;Institute of Policy and Management, Chinese Academy of Sciences, No. 55 Zhongguancun East Road, Beijing 100190, China;Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, No. 80 Zhongguancun East Road, Haidian District, Beijing 100080, China and College of Information Science and T ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

The standard Support Vector Machine (SVM) minimizes the @e-insensitive loss function subject to L"2 penalty, which equals solving a quadratic programming. While the least squares support vector machine (LS-SVM) considers equality constraints instead of inequality constrains, which corresponds to solving a set of linear equations to reduce computational complexity, loses sparseness and robustness. These two learning methods are non-adaptive since their penalty functions are pre-defined in a top-down manner, which do not work well in all situations. In this paper, we try to solve these two drawbacks and propose a weighted L"q adaptive LS-SVM model (WL"q-LS-SVM) classifiers that combines the prior knowledge and adaptive learning process, which adaptively chooses q according to the data set structure. An evolutionary strategy-based algorithm is suggested to solve the WL"q-LS-SVM. Simulation and real data tests have shown the effectiveness of our method.