Nearest convex hull classification

Georgi Nalbantov, Patrick Groenen (groenen@ese.eur.nl) and Cor Bioch

No EI 2006-50, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute

Abstract: Consider the classification task of assigning a test object to one of two or more possible groups, or classes. An intuitive way to proceed is to assign the object to that class, to which the distance is minimal. As a distance measure to a class, we propose here to use the distance to the convex hull of that class. Hence the name Nearest Convex Hull (NCH) classification for the method. Convex-hull overlap is handled through the introduction of slack variables and kernels. In spirit and computationally the method is therefore close to the popular Support Vector Machine (SVM) classifier. Advantages of the NCH classifier are its robustness to outliers, good regularization properties and relatively easy handling of multi-class problems. We compare the performance of NCH against state-of-art techniques and report promising results.

Date: 2006-12-01
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://repub.eur.nl/pub/8217/NCH10.pdf (application/pdf)

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:ems:eureir:8217

Access Statistics for this paper

More papers in Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute Contact information at EDIRC.
Bibliographic data for series maintained by RePub (peter.vanhuisstede@eur.nl this e-mail address is bad, please contact repec@repec.org).