Multiclass Optimal Classification Trees with SVM-splits
This work addresses multiclass classification problems for data scientists, presenting an incremental improvement by combining existing techniques in a new way.
The authors tackled multiclass classification by developing a novel tree-based method that uses SVM hyperplanes to group labels at internal nodes, and they reported performance results from extensive computational experiments.
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.