2018/10/15 by Murat Fırat, Murat Firat, Firat, Murat +9
Computer Science · Mathematics · #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Metaheuristic Optimization Algorithms Research #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1810.06684
openalex publication_date 2018/10/15 · arxiv created 2019/07/11 · arxiv updated 2019/07/12 · openalex created_date 2019/08/13 · openalex updated_date 2026/07/28
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.