ITER: an algorithm for predictive regression rule extraction

Authors:
Johan Huysmans;Bart Baesens;Jan Vanthienen
Affiliations:
Dept. of Decision Sciences and Information Management, K.U.Leuven, Leuven, Belgium;Dept. of Decision Sciences and Information Management, K.U.Leuven, Leuven, Belgium;Dept. of Decision Sciences and Information Management, K.U.Leuven, Leuven, Belgium
Venue:
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Year:
2006

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Abstract

Various benchmarking studies have shown that artificial neural networks and support vector machines have a superior performance when compared to more traditional machine learning techniques. The main resistance against these newer techniques is based on their lack of interpretability: it is difficult for the human analyst to understand the motivation behind these models’ decisions. Various rule extraction techniques have been proposed to overcome this opacity restriction. However, most of these extraction techniques are devised for classification and only few algorithms can deal with regression problems. In this paper, we present ITER, a new algorithm for pedagogical regression rule extraction. Based on a trained ‘black box’ model, ITER is able to extract human-understandable regression rules. Experiments show that the extracted model performs well in comparison with CART regression trees and various other techniques.