Multiclass optimal classification trees with SVM-splits
Víctor Blanco, Alberto Japón, Justo Puerto
Abstract
Abstract In this paper we present a novel mathematical optimization-based methodology to construct tree-shaped classification rules for multiclass instances. Our approach consists of building Classification Trees in which, except for the leaf nodes, the labels are temporarily left out and grouped into two classes by means of a SVM separating hyperplane. We provide a Mixed Integer Non Linear Programming formulation for the problem and report the results of an extended battery of computational experiments to assess the performance of our proposal with respect to other benchmarking classification methods.
Topics & Concepts
Multiclass classificationHyperplaneSupport vector machineInteger programmingConstruct (python library)BenchmarkingClass (philosophy)Artificial intelligenceMathematicsTree (set theory)Machine learningLinear programmingComputer sciencePattern recognition (psychology)Mathematical optimizationCombinatoricsProgramming languageMarketingBusinessMachine Learning and Data ClassificationImbalanced Data Classification TechniquesData Stream Mining Techniques