Publication
Title
An outlier map for support vector machine classification
Author
Abstract
Support Vector Machines are a widely used classification technique. They are computationally efficient and provide excellent predictions even for high-dimensional data. Moreover, Support Vector Machines are very flexible due to the incorporation of kernel functions. The latter allow to model nonlinearity, but also to deal with nonnumerical data such as protein strings. However, Support Vector Machines can suffer a lot from unclean data containing, for example, outliers or mislabeled observations. Although several outlier detection schemes have been proposed in the literature, the selection of outliers versus nonoutliers is often rather ad hoc and does not provide much insight in the data. In robust multivariate statistics outlier maps are quite popular tools to assess the quality of data under consideration. They provide a visual representation of the data depicting several types of outliers. This paper proposes an outlier map designed for Support Vector Machine classification. The Stahel-Donoho outlyingness measure from multivariate statistics is extended to an arbitrary kernel space. A trimmed version of Support Vector Machines is defined trimming part of the samples with largest outlyingness. Based on this classifier, an outlier map is constructed visualizing data in any type of high-dimensional kernel space. The outlier map is illustrated on 4 biological examples showing its use in exploratory data analysis.
Language
English
Source (journal)
Annals of applied statistics. - Cleveland, Ohio
Publication
Cleveland, Ohio : 2009
ISSN
1932-6157
Volume/pages
3:4(2009), p. 1566-1580
ISI
000283528200022
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 01.03.2012
Last edited 14.10.2017
To cite this reference