Study of Double SMO Algorithm Based on Attributes Reduction

To solve the classification problem in data mining, this paper proposes double SMO algorithm based on attributes reduction. Firstly attributes reduction deletes irrelevant attributes (or dimensions) to reduce data amount, consequently the total calculation is reduced, the training speed is fastened and Classification mode is easy to understand. Secondly applying SMO algorithm on the sampling dataset to get the approximate separating hyperplane, and then we obtain all the support vectors of original dataset. Finally again use SMO algorithm on the support vectors to get the final separating hyperplane. It is shown in the experiments that the algorithm reduces the memory space, effectively avoids the noise points' effect on the final separating hyperplane and the precision of the algorithm is better than Decision Tree, Bayesian and Neural Network.

[1]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[2]  David R. Musicant,et al.  Successive overrelaxation for support vector machines , 1999, IEEE Trans. Neural Networks.

[3]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[5]  Susan I. Hruska,et al.  Back-propagation learning in expert networks , 1992, IEEE Trans. Neural Networks.

[6]  Yaxin Bi,et al.  KNN Model-Based Approach in Classification , 2003, OTM.

[7]  K G Zografos,et al.  An operational centre for managing major chemical industrial accidents. , 2002, Journal of hazardous materials.

[8]  Pedro M. Domingos,et al.  Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier , 1996, ICML.

[9]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.