Optimal Decision Diagrams for Classification [article]

Alexandre M. Florio, Pedro Martins, Maximilian Schiffer, Thiago Serra, Thibaut Vidal
2022 arXiv   pre-print
Decision diagrams for classification have some notable advantages over decision trees, as their internal connections can be determined at training time and their width is not bound to grow exponentially with their depth. Accordingly, decision diagrams are usually less prone to data fragmentation in internal nodes. However, the inherent complexity of training these classifiers acted as a long-standing barrier to their widespread adoption. In this context, we study the training of optimal
more » ... diagrams (ODDs) from a mathematical programming perspective. We introduce a novel mixed-integer linear programming model for training and demonstrate its applicability for many datasets of practical importance. Further, we show how this model can be easily extended for fairness, parsimony, and stability notions. We present numerical analyses showing that our model allows training ODDs in short computational times, and that ODDs achieve better accuracy than optimal decision trees, while allowing for improved stability without significant accuracy losses.
arXiv:2205.14500v1 fatcat:r3ufdd7jkrdjbojbnkjxlfnuqm