Rule-based approximation of black-box classifiers for tabular data to generate global and local explanations
Cezary Maszczyk, Michal Kozielski, Marek Sikora
Citation: Proceedings of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 30, pages 89–92 (2022)
Abstract. The need to understand the decision bases of artificial intelligence methods is becoming widespread. One method to obtain explanations of machine learning models and their decisions is the approximation of a complex model treated as a black box by an interpretable rule-based model. Such an approach allows detailed and understandable explanations to be generated from the elementary conditions contained in the rule premises. However, there is a lack of research on the evaluation of such an approximation and the influence of the parameters of the rule-based approximator. In this work, a rule-based approximation of complex classifier for tabular data is evaluated.Moreover, it was investigated how selected measures of rule quality affect the approximation. The obtained results show what quality of approximation can be expected and indicate which measure of rule quality is worth using in such application.
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