A probabilistic support vector machine for uncertain data

A probabilistic support vector machine (PSVM) is proposed for classification of data with uncertainties. Performance of the traditional SVM algorithm is very sensitive to uncertainties. The noises in input space will cause uncertainties of the mapping in feature space. The traditional SVM algorithm may not be effective when uncertainty is large. A new probabilistic optimization is proposed to determine the decision boundary. The minimal distance is described probabilistically by its probability distribution function. Finally an artificial dataset and a real life dataset from UCI machine learning database are used to demonstrate the effectiveness of the proposed PSVM.

[1]  Su-Yun Huang,et al.  Reduced Support Vector Machines: A Statistical Theory , 2007, IEEE Transactions on Neural Networks.

[2]  Sheng-De Wang,et al.  Fuzzy support vector machines , 2002, IEEE Trans. Neural Networks.

[3]  Johan A. K. Suykens,et al.  Optimal control by least squares support vector machines , 2001, Neural Networks.

[4]  Johan A. K. Suykens,et al.  Handling missing values in support vector machine classifiers , 2005, Neural Networks.

[5]  K. Shima,et al.  SVM-based feature selection of latent semantic features , 2004, Pattern Recognit. Lett..

[6]  Kin Keung Lai,et al.  A new fuzzy support vector machine to evaluate credit risk , 2005, IEEE Transactions on Fuzzy Systems.

[7]  Isabelle Guyon,et al.  Discovering Informative Patterns and Data Cleaning , 1996, Advances in Knowledge Discovery and Data Mining.

[8]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[9]  Xuegong Zhang,et al.  Using class-center vectors to build support vector machines , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[10]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[11]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[12]  H. P. Huang,et al.  Fuzzy Support Vector Machines for Pattern Recognition and Data Mining , 2002 .

[13]  Tai-Yue Wang,et al.  Fuzzy support vector machine for multi-class text categorization , 2007, Inf. Process. Manag..

[14]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[15]  Constantin Zopounidis,et al.  Additive Support Vector Machines for Pattern Classification , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).