Optimal arrangements of hyperplanes for SVM-based multiclass classification Blanco Izquierdo, Víctor Japón, Alberto Puerto, Justo In this paper, we present a novel SVM-based approach to construct multiclass classifiers by means of arrangements of hyperplanes. We propose different mixed integer (linear and non linear) programming formulations for the problem using extensions of widely used measures for misclassifying observations where the kernel trick can be adapted to be applicable. Some dimensionality reductions and variable fixing strategies are also developed for thesemodels. An extensive battery of experiments has been run which reveal the powerfulness of our proposal as compared with other previously proposed methodologies. 2024-01-22T08:32:36Z 2024-01-22T08:32:36Z 2020-03 journal article Published version: Advances in Data Analysis and Classification 14, p 175-199. https://doi.org/10.1007/s11634-019-00367-6 https://hdl.handle.net/10481/87031 10.1007/s11634-019-00367-6 eng open access Springer