ITER: An Algorithm for Predictive Regression Rule Extraction
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.
KeywordsSupport Vector Machine Mean Absolute Error Rule Extraction Regression Rule Training Observation
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- 2.Barakat, N., Diederich, J.: Eclectic rule-extraction from support vector machines. International Journal of Computational Intelligence 2(1), 59–62 (2005)Google Scholar
- 3.Breiman, L., Friedman, J.H., Olsen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth and Brooks (1984)Google Scholar
- 4.Fung, G., Sandilya, S., Rao, R.B.: Rule extraction from linear support vector machines. In: 11th ACM SIGKDD international conference on Knowledge discovery in data mining, pp. 32–40 (2005)Google Scholar
- 5.Martens, D., Baesens, B., Van Gestel, T., Vanthienen, J.: Adding comprehensibility to support vector machines using rule extraction techniques. In: Credit Scoring and Credit Control IX (2005)Google Scholar
- 6.Núñez, H., Angulo, C., Català, A.: Rule extraction from support vector machines. In: European Symposium on Artificial Neural Networks (ESANN), pp. 107–112 (2002)Google Scholar
- 7.Quinlan, J.R.: Learning with Continuous Classes. In: 5th Australian Joint Conference on Artificial Intelligence, pp. 343–348 (1992)Google Scholar
- 10.Viaene, S., Derrig, R., Baesens, B., Dedene, G.: A comparison of state-of-the-art classification techniques for expert automobile insurance fraud detection. Journal of Risk And Insurance (Special Issue on Fraud Detection) 69(3), 433–443 (2002)Google Scholar