Optimal arrangements of hyperplanes for SVM-based multiclass classification

被引：16

作者：

Blanco, Victor ^{[1
]}

Japon, Alberto ^{[2
]}

Puerto, Justo ^{[2
]}

机构：

[1] Univ Granada, IEMath GR, E-18001 Granada, Spain

[2] Univ Seville, IMUS, E-41012 Seville, Spain

来源：

ADVANCES IN DATA ANALYSIS AND CLASSIFICATION | 2020年 / 14卷 / 01期

关键词：

Multiclass support vector machines; Mixed integer non linear programming; Classification; hyperplanes; SUPPORT VECTOR MACHINES; FEATURE-SELECTION; NEAREST-NEIGHBOR; PREDICTION;

D O I：

10.1007/s11634-019-00367-6

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：175 / 199

页数：25

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