Publication
Title
Large-scale classification by an approximate least squares one-class support vector machine ensemble
Author
Abstract
Large-scale multi-class classification problems involve an enormous amount of training data that make the application of classical non-linear classification algorithms difficult. In addition, such multi-class classification problems are usually formed by a considerable number of classes. This makes the application of the popular one-versus-rest binary classifiers fusion scheme adopted by most state-of-the-art approaches difficult. In this paper, in order to overcome the high computational cost of multi-class non-linear classification approaches, we adopt an ensemble of approximate non-linear one-class classifiers. To this end, we propose a new scalable solution for the Least Squares One-Class Support Vector Machine classifier by following an approximate kernel approach. We evaluated the proposed method in big data visual classification problems, where it is shown that it is able to achieve satisfactory performance, while significantly reducing the overall computational and memory costs.
Language
English
Source (book)
2015 IEEE Trustcom/BigDataSE/ISPA, 20-22 August, 2015, Helsinki, Finland
Publication
New york : 2015
ISBN
978-1-4673-7952-6
978-1-4673-7952-6
DOI
10.1109/TRUSTCOM.2015.555
Volume/pages
(2015) , p. 6-10
ISI
000391000900002
Full text (Publisher's DOI)
UAntwerpen
Publication type
Subject
External links
Web of Science
Record
Identifier
Creation 17.10.2023
Last edited 20.08.2024
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