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Optimal arrangements of hyperplanes for SVM-based multiclass classification. (English) Zbl 07205278
Summary: 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.
MSC:
62H30 Classification and discrimination; cluster analysis (statistical aspects)
90C11 Mixed integer programming
68T05 Learning and adaptive systems in artificial intelligence
32S22 Relations with arrangements of hyperplanes
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