A Tree-Based Multi-class SVM Classifier for Digital Library Document

In this paper, we present a new method of using support vector machine (SVM) for multiclass classification. In our method, we use a tree based SVM classifier for classification. Compared with the other SVM multi-class classification methods in literature (i.e. one-against-one, DAGSVM), our proposed SVM tree classifier is more efficient in both training/classification. Our new SVM tree classifier requires o(n) SVM training during the training stage and O(log(n)) SVM testing during the test stage, while other methods require o(n2) or at best o(n) SVM training during the training and O(n2) or at best O(n) SVM testing during testing. Experimental results on digital library document classification demonstrate that our methods is not only significantly more efficient but also achieves the similar precision of classification.

[1]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[2]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[3]  J. Farkas,et al.  Generating document clusters using thesauri and neural networks , 1994, 1994 Proceedings of Canadian Conference on Electrical and Computer Engineering.

[4]  Takenobu Tokunaga,et al.  Hierarchical Bayesian Clustering for Automatic Text Classification , 1995, IJCAI.

[5]  Børge Svingen.  Using Genetic Programming for Document Classification , 1998, FLAIRS Conference.

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

[7]  Tomaso A. Poggio,et al.  A general framework for object detection , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[8]  M. Benkhalifa,et al.  Text categorization using the semi-supervised fuzzy c-means algorithm , 1999, 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397).

[9]  David G. Stork,et al.  Pattern Classification , 1973 .

[10]  Thorsten Joachims,et al.  Text categorization with support vector machines , 1999 .

[11]  Wai Lam,et al.  Automatic document classification based on probabilistic reasoning: model and performance analysis , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[12]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

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

[14]  Tet Hin Yeap,et al.  ECG Beat Classification By A Neural Network , 1990, [1990] Proceedings of the Twelfth Annual International Conference of the IEEE Engineering in Medicine and Biology Society.