AIKED'11 Proceedings of the 10th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Polytope classifier: a symbolic knowledge extraction from piecewise-linear support vector machine
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
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This paper presents a new approach to rule extraction from Support Vector Machines (SVMs). SVMs have been applied successfully in many areas with excellent generalization results; rule extraction can offer explanation capability to SVMs. We propose to approximate the SVM classification boundary by solving an optimization problem through sampling and querying followed by boundary searching, rule extraction and post-processing. A theorem and experimental results then indicate that the rules can be used to validate the SVM with high accuracy and very high fidelity.