Support Vector Machines for Unbalanced Multicategory Classification

Kang-Mo Jung

Mathematical Problems in Engineering, 2015, vol. 2015, 1-7

Abstract:

Classification is a very important research topic and its applications are various, because data can be easily obtained in these days. Among many techniques of classification the support vector machine (SVM) is widely applied to bioinformatics or genetic analysis, because it gives sound theoretical background and its performance is superior to other methods. The SVM can be rewritten by a combination of the hinge loss function and the penalty function. The smoothly clipped absolute deviation penalty function satisfies desirably statistical properties. Since standard SVM techniques typically treat all classes equally, it is not well suited to unbalanced proportion data. We propose a robust method to treat unbalanced cases based on the weights of the class. Simulation and a numerical example show that the proposed method is effective to analyze unbalanced proportion data.

Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2015/294985.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2015/294985.xml (text/xml)

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:hin:jnlmpe:294985

DOI: 10.1155/2015/294985

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().