Large-Scale Classification by an Approximate Least Squares One-Class Support Vector Machine Ensemble

Vasileios Mygdalis, Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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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.
Original languageEnglish
Title of host publication2015 IEEE Trustcom/BigDataSE/ISPA
Subtitle of host publicationProceedings of a meeting held 20-22 August 2015, Helsinki, Finland
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages6-10
Number of pages5
Volume2
ISBN (Electronic)9781467379526
ISBN (Print)9781467379533
DOIs
Publication statusPublished - Jan 2016
EventIEEE International Conference on Big Data Science and Engineering (BigDataSE) - Helsinki, Finland
Duration: 20 Aug 201522 Aug 2015

Conference

ConferenceIEEE International Conference on Big Data Science and Engineering (BigDataSE)
Country/TerritoryFinland
CityHelsinki
Period20/08/1522/08/15

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