bsnsing: A decision tree induction method based on recursive optimal boolean rule composition
This work addresses the challenge of efficient and accurate decision tree induction for data analysts and researchers, offering an incremental improvement over existing optimal tree methods by focusing on split optimization rather than whole-tree optimization.
The paper tackled the problem of optimizing split rule selection in decision tree induction by proposing a novel mixed-integer programming formulation that directly maximizes Gini reduction, resulting in bsnsing trees that outperformed other decision tree codes in discrimination on 75 datasets and offered faster training and broader applicability without losing accuracy.
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 which 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 to trees trained by other decision tree codes including the rpart, C50, party and tree packages in R. Compared to 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.