On support vector machines under a multiple-cost scenario

Sandra Benítez-Peña (), Rafael Blanquero, Emilio Carrizosa and Pepa Ramírez-Cobo
Additional contact information
Sandra Benítez-Peña: IMUS, Instituto de Matemáticas de la Universidad de Sevilla
Rafael Blanquero: IMUS, Instituto de Matemáticas de la Universidad de Sevilla
Emilio Carrizosa: IMUS, Instituto de Matemáticas de la Universidad de Sevilla
Pepa Ramírez-Cobo: IMUS, Instituto de Matemáticas de la Universidad de Sevilla

Advances in Data Analysis and Classification, 2019, vol. 13, issue 3, No 5, 663-682

Abstract: Abstract Support vector machine (SVM) is a powerful tool in binary classification, known to attain excellent misclassification rates. On the other hand, many realworld classification problems, such as those found in medical diagnosis, churn or fraud prediction, involve misclassification costs which may be different in the different classes. However, it may be hard for the user to provide precise values for such misclassification costs, whereas it may be much easier to identify acceptable misclassification rates values. In this paper we propose a novel SVM model in which misclassification costs are considered by incorporating performance constraints in the problem formulation. Specifically, our aim is to seek the hyperplane with maximal margin yielding misclassification rates below given threshold values. Such maximal margin hyperplane is obtained by solving a quadratic convex problem with linear constraints and integer variables. The reported numerical experience shows that our model gives the user control on the misclassification rates in one class (possibly at the expense of an increase in misclassification rates for the other class) and is feasible in terms of running times.

Keywords: Constrained classification; Misclassification costs; Mixed integer quadratic programming; Sensitivity/specificity trade-off; Support vector machines; 62P99; 90C11; 90C30 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s11634-018-0330-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advdac:v:13:y:2019:i:3:d:10.1007_s11634-018-0330-5

Ordering information: This journal article can be ordered from
http://www.springer. ... ds/journal/11634/PS2

DOI: 10.1007/s11634-018-0330-5

Access Statistics for this article

Advances in Data Analysis and Classification is currently edited by H.-H. Bock, W. Gaul, A. Okada, M. Vichi and C. Weihs

More articles in Advances in Data Analysis and Classification from Springer, German Classification Society - Gesellschaft für Klassifikation (GfKl), Japanese Classification Society (JCS), Classification and Data Analysis Group of the Italian Statistical Society (CLADAG), International Federation of Classification Societies (IFCS)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().