Treffer: Two-group classification via a biobjective margin maximization model : Optimization and data mining

Title:
Two-group classification via a biobjective margin maximization model : Optimization and data mining
Source:
European journal of operational research. 173(3):746-761
Publisher Information:
Amsterdam: Elsevier, 2006.
Publication Year:
2006
Physical Description:
print, 18 ref
Original Material:
INIST-CNRS
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Departamento de Estadistica e Investigación Operativa, Universidad de Sevilla, Avda. Reina Mercedes, s/n, 41012 Sevilla, Spain
ISSN:
0377-2217
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Operational research. Management
Accession Number:
edscal.17882193
Database:
PASCAL Archive

Weitere Informationen

In this paper we propose a biobjective model for two-group classification via margin maximization, in which the margins in both classes are simultaneously maximized. The set of Pareto-optimal solutions is described, yielding a set of parallel hyperplanes, one of which is just the solution of the classical SVM approach. In order to take into account different misclassification costs or a priori probabilities, the ROC curve can be used to select one out of such hyperplanes by expressing the adequate tradeoff for sensitivity and specificity. Our result gives a theoretical motivation for using the ROC approach in case misclassification costs in the two groups are not necessarily.