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Rule Extraction from Decision Trees Ensembles: New Algorithms Based on Heuristic Search and Sparse Group Lasso Methods

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  • Morteza Mashayekhi

    (School of Computer Science, University of Windsor, 401 Sunset Ave., Windsor, ON N9B3P4, Canada)

  • Robin Gras

    (School of Computer Science, University of Windsor, 401 Sunset Ave., Windsor, ON N9B3P4, Canada)

Abstract

Decision trees are examples of easily interpretable models whose predictive accuracy is normally low. In comparison, decision tree ensembles (DTEs) such as random forest (RF) exhibit high predictive accuracy while being regarded as black-box models. We propose three new rule extraction algorithms from DTEs. The RF+DHC method, a hill climbing method with downhill moves (DHC), is used to search for a rule set that decreases the number of rules dramatically. In the RF+SGL and RF+MSGL methods, the sparse group lasso (SGL) method, and the multiclass SGL (MSGL) method are employed respectively to find a sparse weight vector corresponding to the rules generated by RF. Experimental results with 24 data sets show that the proposed methods outperform similar state-of-the-art methods, in terms of human comprehensibility, by greatly reducing the number of rules and limiting the number of antecedents in the retained rules, while preserving the same level of accuracy.

Suggested Citation

  • Morteza Mashayekhi & Robin Gras, 2017. "Rule Extraction from Decision Trees Ensembles: New Algorithms Based on Heuristic Search and Sparse Group Lasso Methods," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(06), pages 1707-1727, November.
    • Handle: RePEc:wsi:ijitdm:v:16:y:2017:i:06:n:s0219622017500055
    DOI: 10.1142/S0219622017500055
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    References listed on IDEAS

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    1. Martens, David & Baesens, Bart & Van Gestel, Tony & Vanthienen, Jan, 2007. "Comprehensible credit scoring models using rule extraction from support vector machines," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1466-1476, December.
    2. Bart Baesens & Rudy Setiono & Christophe Mues & Jan Vanthienen, 2003. "Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation," Management Science, INFORMS, vol. 49(3), pages 312-329, March.
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