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Using Rule Extraction to Improve the Comprehensibility of Predictive Models

Whereas newer machine learning techniques, like artificial neural networks and support vector machines, have shown superior performance in various benchmarking studies, the application of these techniques remains largely restricted to research environments. A more widespread adoption of these techniques is foiled by their lack of explanation capability which is required in some application areas, like medical diagnosis or credit scoring. To overcome this restriction, various algorithms have been proposed to extract a meaningful description of the underlying 'blackbox' models. These algorithms' dual goal is to mimic the behavior of the black box as closely as possible while at the same time they have to ensure that the extracted description is maximally comprehensible. In this research report, we first develop a formal definition of 'rule extraction' and comment on the inherent trade-off between accuracy and comprehensibility. Afterwards, we develop a taxonomy by which rule extraction algorithms can be classified and discuss some criteria by which these algorithms can be evaluated. Finally, an in-depth review of the most important algorithms is given. This report is concluded by pointing out some general shortcomings of existing techniques and opportunities for future research.