Feature scaling in support vector data description

Conference paper (2002)

Authors

P Juszczak ImPhys/Quantitative Imaging
David Tax ImPhys/Quantitative Imaging
Bob Duin ImPhys/Quantitative Imaging
Research Group
ImPhys/Quantitative Imaging
More Info
expand_more

Published Date

2002

Language

Undefined/Unknown

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Research Group

ImPhys/Quantitative Imaging

Abstract

When in a classification problem only samples of one class are easily accessible, this problem is called a one-class classification problem. Many standard classifiers, like backpropagation neural networks, fail on this data. Some other classifiers, like k-means clustering or nearest neighbor classifier can be applied after some minor changes. In this paper we focus on the support vector data description classifier, which is especially constructed for one-class classification. But this method appears to be sensitive to scaling of the individual features of the dataset. We show that it is possible to improve its performance by adequate scaling of the feature space. Some results will be shown on artificial dataset and handwritten digits dataset.