@misc{10481/85274,
year = {2023},
month = {9},
url = {https://hdl.handle.net/10481/85274},
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.},
organization = {Universidad de Sevilla/CBUA},
organization = {Spanish Ministerio de Ciencia y Tecnología, Agencia Estatal de Investigación, and
Fondos Europeos de Desarrollo Regional (FEDER) via project PID2020-114594GB-C21},
organization = {Junta de Andalucía
projects FEDER-US-1256951, P18-FR-1422, CEI-3-FQM331, B-FQM-322-UGR20},
organization = {AT 21_00032;
Fundación BBVA through project NetmeetData: Big Data 2019},
organization = {UE-NextGenerationEU (ayudas de movilidad
para la recualificación del profesorado universitario)},
organization = {IMAG-Maria de Maeztu grant CEX2020-
001105-M /AEI /10.13039/501100011033},
publisher = {Springer Nature},
keywords = {Supervised classification},
keywords = {Optimal classification trees},
keywords = {Multiclass},
title = {Multiclass optimal classification trees with SVM‑splits},
doi = {10.1007/s10994-023-06366-1},
author = {Blanco Izquierdo, Víctor and Japón, Alberto and Puerto, Justo},
}