Fast Support Vector Machine Classification for Large Data Sets

DOI

10.1080/18756891.2013.868148How to use a DOI?

Keywords

support vector machine, classification, large data sets

Abstract

Normal support vector machine (SVM) algorithms are not suitable for classification of large data sets because of high training complexity. This paper introduces a novel two-stage SVM classification approach for large data sets. Fast clustering techniques are introduced to select the training data from the original data set for the first stage SVM, and a de-clustering technique is then proposed to recover the training data for the second stage SVM. The proposed two-stage SVM classifier has distinctive advantages on dealing with huge data sets such as those in bioinformatics. Finally, we apply the proposed method on several benchmark problems. Experimental results demonstrate that our approach has good classification accuracy while the training is significantly faster than other SVM classifiers.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Xiaoou Li
AU  - Wen Yu
PY  - 2014
DA  - 2014/04/01
TI  - Fast Support Vector Machine Classification for Large Data Sets
JO  - International Journal of Computational Intelligence Systems
SP  - 197
EP  - 212
VL  - 7
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.868148
DO  - 10.1080/18756891.2013.868148
ID  - Li2014
ER  -