Title :
A new rule extraction approach from Support Vector Machines
Author :
Si Xiao Yang ; Ying Jie Tian
Author_Institution :
Res. Center on Fictitious Econ. & Data Sci., CAS Beijing, Beijing, China
Abstract :
Support Vector Machines have been promising tools for data mining during these years because of their good performance. However, a main weakness of SVMs is lack of comprehensibility: people can not understand what the “optimal hyperplane” means and are unconfident about the prediction especially when they are not the domain experts. In this paper 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 our algorithm.
Keywords :
data mining; decision trees; knowledge acquisition; support vector machines; comprehensibility; data mining; decision tree algorithm; knowledge extraction; optimal hyperplane; rule extraction; support vector machine; Accuracy; Data mining; Decision trees; Prediction algorithms; Silicon; Support vector machines; Training data;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-61284-180-9
DOI :
10.1109/FSKD.2011.6019744
