Learning-Based Rule-Extraction From Support Vector Machines: Performance On Benchmark Data Sets

Abstract

Over the last decade, rule-extraction from neural networks (ANN) techniques have been developed to explain how classification and regression are realised by the ANN. Yet, this is not the case for support vector machines (SVMs) which also demonstrate an inability to explain the process by which a learning result was reached and why a decision is being made. Rule-extraction from SVMs is important, especially for applications such as medical diagnosis. In this paper, an approach for learning-based rule-extraction from support vector machines is outlined, including an evaluation of the quality of the extracted rules in terms of fidelity, accuracy, consistency and comprehensibility. In addition, the rules are verified by use of knowledge from the problem domains as well as other classification techniques to assure correctness and validity