Support vector machine with SOM-based quasi-linear kernel for nonlinear classification

This paper proposes a self-organizing maps (SOM) based kernel composition method for the quasi-linear support vector machine (SVM). The quasi-linear SVM is SVM model with quasi-linear kernel, in which the nonlinear separation hyperplane is approximated by multiple local linear models with interpolation. The basic idea underlying the proposed method is to use clustering and projection properties of SOM to partition the input space and construct a SOM based quasi-linear kernel. By effectively extracting the distribution information using SOM, the quasi-linear SVM with the SOM-based quasi-linear kernel is expected to have better performance in the cases of high-noise and high-dimension. Experiment results on synthetic datasets and real world datasets show the effectiveness of the proposed method.

[1]  Stefan Klanke,et al.  PSOM+ : Parametrized Self-Organizing Maps for noisy and incomplete data , 2005 .

[2]  José Alfredo Ferreira Costa,et al.  Clustering, Noise Reduction and Visualization Using Features Extracted from the Self-Organizing Map , 2013, IDEAL.

[3]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[4]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[5]  Yu Cheng,et al.  Nonlinear system identification based on SVR with quasi-linear kernel , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[6]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[7]  Grigorios Tsoumakas,et al.  Protein Classification with Multiple Algorithms , 2005, Panhellenic Conference on Informatics.

[8]  Arian Maleki,et al.  Geodesic K-means clustering , 2008, 2008 19th International Conference on Pattern Recognition.

[9]  Francis Eng Hock Tay,et al.  Improved financial time series forecasting by combining Support Vector Machines with self-organizing feature map , 2001, Intell. Data Anal..

[10]  Andreas Rauber,et al.  The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data , 2002, IEEE Trans. Neural Networks.

[11]  Kuang Yin,et al.  Fault Pattern Recognition of Thermodynamic System Based on SOM , 2010, 2010 International Conference on Electrical and Control Engineering.

[12]  G H Ball,et al.  A clustering technique for summarizing multivariate data. , 1967, Behavioral science.

[13]  Erzsébet Merényi,et al.  Exploiting Data Topology in Visualization and Clustering of Self-Organizing Maps , 2009, IEEE Transactions on Neural Networks.

[14]  Jinglu Hu,et al.  Local linear multi-SVM method for gene function classification , 2010, 2010 Second World Congress on Nature and Biologically Inspired Computing (NaBIC).

[15]  John W. Sammon,et al.  A Nonlinear Mapping for Data Structure Analysis , 1969, IEEE Transactions on Computers.

[16]  Andreas König,et al.  Interactive visualization and analysis of hierarchical neural projections for data mining , 2000, IEEE Trans. Neural Networks Learn. Syst..

[17]  Guido Deboeck.  Financial Applications of Self-Organizing Maps , 1998 .

[18]  Vladimir Estivill-Castro,et al.  Fast and Robust General Purpose Clustering Algorithms , 2000, Data Mining and Knowledge Discovery.

[19]  Robert E. Schapire,et al.  Hierarchical multi-label prediction of gene function , 2006, Bioinform..

[20]  Miguel A. Sanz-Bobi,et al.  Auto-Regressive Processes Explained by Self-Organized Maps. Application to the Detection of Abnormal Behavior in Industrial Processes , 2011, IEEE Transactions on Neural Networks.

[21]  Amiya Kumar Rath,et al.  A hybridized K-means clustering approach for high dimensional dataset , 2010 .

[22]  Baxter P. Rogers,et al.  Unsupervised Spatiotemporal Analysis of FMRI Data Using Graph-Based Visualizations of Self-Organizing Maps , 2013, IEEE Transactions on Biomedical Engineering.

[23]  Jinglu Hu,et al.  An SVM-based approach for stock market trend prediction , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[24]  Juha Vesanto,et al.  SOM-based data visualization methods , 1999, Intell. Data Anal..