NDLI: Multi-Classification by Using Tri-Class SVM

Content Provider	Springer Nature Link
Author	Angulo, Cecilio Ruiz, Francisco J. González, Luis Ortega, Juan Antonio
Copyright Year	2006
Abstract	The standard form for dealing with multi-class classification problems when bi-classifiers are used is to consider a two-phase (decomposition, reconstruction) training scheme. The most popular decomposition procedures are pairwise coupling (one versus one, 1-v-1), which considers a learning machine for each Pair of classes, and the one-versus-all scheme (one versus all, 1-v-r), which takes into consideration each class versus the remaining classes. In this article a 1-v-1 tri-class Support Vector Machine (SVM) is presented. The expansion of the architecture of this machine into three categories specifically addresses the decomposition problem of how to prevent the loss of information which occurs in the usual 1-v-1 training procedure. The proposed machine, by means of a third class, allows all the information to be incorporated into the remaining training patterns when a multi-class problem is considered in the form of a 1-v-1 decomposition. Three general structures are presented where each improves some features from the precedent structure. In order to deal with multi-classification problems, it is demonstrated that the final machine proposed allows ordinal regression as a form of decomposition procedure. Examples and experimental results are presented which illustrate the performance of the new tri-class SV machine.
Starting Page	89
Ending Page	101
Page Count	13
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	23
Issue Number	1
e-ISSN	1573773X
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2006-01-01
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	bi-classifier multi-classification ordinal regression Support Vector Machine Artificial Intelligence (incl. Robotics) Electronic and Computer Engineering Operations Research/Decision Theory Nonlinear Dynamics, Complex Systems, Chaos, Neural Networks
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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