Improving SVM Classification on Imbalanced Datasets by Introducing a New Bias

Improving SVM Classification on Imbalanced Datasets by Introducing a New Bias Support Vector Machine (SVM) learning from imbalanced datasets, as well as most learning machines, can show poor performance on the minority class because SVMs were designed to induce a model based on the overall error. To improve their performance in these kind of problems, a low-cost post-processing strategy is proposed based on calculating a new bias to adjust the function learned by the SVM. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Classification Springer Journals

Improving SVM Classification on Imbalanced Datasets by Introducing a New Bias

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Publisher
Springer US
Copyright
Copyright © 2017 by Classification Society of North America
Subject
Statistics; Statistical Theory and Methods; Pattern Recognition; Bioinformatics; Signal,Image and Speech Processing; Psychometrics; Marketing
ISSN
0176-4268
eISSN
1432-1343
D.O.I.
10.1007/s00357-017-9242-x
Publisher site
See Article on Publisher Site

Abstract

Support Vector Machine (SVM) learning from imbalanced datasets, as well as most learning machines, can show poor performance on the minority class because SVMs were designed to induce a model based on the overall error. To improve their performance in these kind of problems, a low-cost post-processing strategy is proposed based on calculating a new bias to adjust the function learned by the SVM.

Journal

Journal of ClassificationSpringer Journals

Published: Oct 14, 2017

References

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