Optimal arrangements of hyperplanes for SVM-based multiclass classification
Víctor Blanco (vblanco@ugr.es),
Alberto Japón (ajapon1@us.es) and
Justo Puerto (puerto@us.es)
Additional contact information
Víctor Blanco: Universidad de Granada
Alberto Japón: Universidad de Sevilla
Justo Puerto: Universidad de Sevilla
Advances in Data Analysis and Classification, 2020, vol. 14, issue 1, No 9, 175-199
Abstract:
Abstract 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 these models. An extensive battery of experiments has been run which reveal the powerfulness of our proposal as compared with other previously proposed methodologies.
Keywords: Multiclass support vector machines; Mixed integer non linear programming; Classification; hyperplanes; 62H30; 90C11; 68T05; 32S22 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advdac:v:14:y:2020:i:1:d:10.1007_s11634-019-00367-6
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DOI: 10.1007/s11634-019-00367-6
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