Column generation based math-heuristic for classification trees
This work addresses classification tasks for data scientists by offering an incremental improvement in decision tree optimization.
The paper tackled constructing univariate binary decision trees for classification by proposing a novel Integer Linear Programming formulation solved via a Column Generation heuristic, and it showed competitive performance with state-of-the-art ILP-based algorithms, handling big data sets with tens of thousands of rows.
This paper explores the use of Column Generation (CG) techniques in constructing univariate binary decision trees for classification tasks. We propose a novel Integer Linear Programming (ILP) formulation, based on root-to-leaf paths in decision trees. The model is solved via a Column Generation based heuristic. To speed up the heuristic, we use a restricted instance data by considering a subset of decision splits, sampled from the solutions of the well-known CART algorithm. Extensive numerical experiments show that our approach is competitive with the state-of-the-art ILP-based algorithms. In particular, the proposed approach is capable of handling big data sets with tens of thousands of data rows. Moreover, for large data sets, it finds solutions competitive to CART.