Title :
An Optimized Multi-class Classification Algorithm Based on SVM Decision Tree
Author :
Donghui, Chen ; Zhijing, Liu
Author_Institution :
Sch. of Comput. Sci. & Technol., Xidian Univ. Xi´´an, Xi´´an, China
Abstract :
An optimized multi-class classification algorithm based on SVM decision tree (SVMDT) is proposed. But by SVMDT, the generalization ability depends on the tree structure. In this paper, the relativity separability measure between classes is defined based on the distribution of the training samples to improve the generalization ability of SVMDT. SVM is extended to non-linear SVM by using kernel functions and the classification experiments prove the algorithm is more effective and feasible for classification accuracy.
Keywords :
decision trees; pattern classification; support vector machines; SVM decision tree; kernel functions; multiclass classification algorithm; relativity separability measurement; support vector machines; Classification algorithms; Classification tree analysis; Kernel; Support vector machine classification; Training; SVM; SVMDT; kernel functions; the relativity separability measure;
Conference_Titel :
Information Technology and Computer Science (ITCS), 2010 Second International Conference on
Conference_Location :
Kiev
Print_ISBN :
978-1-4244-7293-2
Electronic_ISBN :
978-1-4244-7294-9
DOI :
10.1109/ITCS.2010.17
