Fast data selection for SVM training using ensemble margin

Li Guo

doi:10.1016/j.patrec.2014.08.003

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Fast data selection for SVM training using ensemble margin

2015-01-01

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Li Guo *

Samia Boukir

DOI：10.1016/j.patrec.2014.08.003

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Abstract

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Support Vector Machine (SVM) is a powerful classification method. However, it suffers a major drawback: the high memory and time complexity of the training which constrains the application of SVM to large size classification tasks. To accelerate the SVM training, a new ensemble margin-based data selection approach is proposed. It relies on a simple and efficient heuristic to provide support vector candidates: selecting lowest margin instances. This technique significantly reduces the SVM training task complexity while maintaining the accuracy of the SVM classification. A fast alternative of our approach we called SVIS (Small Votes Instance Selection) with great potential for large data problem is also introduced. (C) 2014 Elsevier B.V. All rights reserved.

Keywords:

Boundary points

Instance selection

Ensemble learning

Margin theory

Large data

Support vector machine

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