Knowledge Extraction from Support Vector Machines

Yong Shi (), Lingling Zhang, Yingjie Tian () and Xingsen Li
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Yong Shi: Chinese Academy of Sciences
Lingling Zhang: University of Chinese Academy of Sciences
Yingjie Tian: Chinese Academy of Sciences
Xingsen Li: Zhejiang University

Chapter 6 in Intelligent Knowledge, 2015, pp 101-111 from Springer

Abstract: Abstract Support Vector Machines have been a promising tool for data mining during these years because of its good performance. However, a main weakness of SVMs is its lack of comprehensibility: people cannot understand what the “optimal hyperplane” means and are unconfident about the prediction especially when they are not the domain experts. In this section we introduce a new method to extract knowledge with a thought inspired by the decision tree algorithm and give a formula to find the optimal attributes for rule extraction. The experimental results will show the efficiency of this method.

Keywords: Support Vector Machine; Decision Function; Rule Extraction; Split Point; Decision Tree Algorithm (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/978-3-662-46193-8_6

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