Novel Design of Decision-Tree-Based Support Vector Machines Multi-class Classifier

Authors:
Liaoying Zhao;Xiaorun Li;Guangzhou Zhao
Affiliations:
Institute of Computer Application Technology, HangZhou Dianzi University, Hangzhou 310018, China;College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China;College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Venue:
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Year:
2009

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Abstract

Designing the hierarchical structure is a key issue for the decision-tree-based (DTB) support vector machines multi-class classification. Inter-class separability is an important basis for designing the hierarchical structure. A new method based on vector projection is proposed to measure inter-class separability. Furthermore, two different DTB support vector multi-class classifiers are designed based on the inter-class separability: one is in the structure of DTB-balanced branches and another is in the structure of DTB-one against all. Experiment results on three large-scale data sets indicate that the proposed method speeds up the decision-tree-based support vector machines multi-class classifiers and yields higher precision.