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bsnsing: A Decision Tree Induction Method Based on Recursive Optimal Boolean Rule Composition

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  • Yanchao Liu

    (Department of Industrial and Systems Engineering, Wayne State University, Detroit, Michigan 48202)

Abstract

This paper proposes a new mixed-integer programming (MIP) formulation to optimize split rule selection in the decision tree induction process and develops an efficient search algorithm that is able to solve practical instances of the MIP model faster than commercial solvers. The formulation is novel for it directly maximizes the Gini reduction, an effective split selection criterion that has never been modeled in a mathematical program for its nonconvexity. The proposed approach differs from other optimal classification tree models in that it does not attempt to optimize the whole tree; therefore, the flexibility of the recursive partitioning scheme is retained, and the optimization model is more amenable. The approach is implemented in an open-source R package named bsnsing. Benchmarking experiments on 75 open data sets suggest that bsnsing trees are the most capable of discriminating new cases compared with trees trained by other decision tree codes including the rpart, C50, party, and tree packages in R. Compared with other optimal decision tree packages, including DL8.5, OSDT, GOSDT, and indirectly more, bsnsing stands out in its training speed, ease of use, and broader applicability without losing in prediction accuracy.

Suggested Citation

  • Yanchao Liu, 2022. "bsnsing: A Decision Tree Induction Method Based on Recursive Optimal Boolean Rule Composition," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 2908-2929, November.
    • Handle: RePEc:inm:orijoc:v:34:y:2022:i:6:p:2908-2929
    DOI: 10.1287/ijoc.2022.1225
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    References listed on IDEAS

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    1. W. Nick Street, 2005. "Oblique Multicategory Decision Trees Using Nonlinear Programming," INFORMS Journal on Computing, INFORMS, vol. 17(1), pages 25-31, February.
    2. Lukas Tanner & Mark Schreiber & Jenny G H Low & Adrian Ong & Thomas Tolfvenstam & Yee Ling Lai & Lee Ching Ng & Yee Sin Leo & Le Thi Puong & Subhash G Vasudevan & Cameron P Simmons & Martin L Hibberd , 2008. "Decision Tree Algorithms Predict the Diagnosis and Outcome of Dengue Fever in the Early Phase of Illness," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 2(3), pages 1-9, March.
    3. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
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