Boosting support vector machines for imbalanced data sets


Real world data mining applications must address the issue of learning from imbalanced data sets. The problem occurs when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed vector spaces or lack of information. Common approaches for… (More)
DOI: 10.1007/s10115-009-0198-y


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@article{Wang2008BoostingSV, title={Boosting support vector machines for imbalanced data sets}, author={Benjamin X. Wang and Nathalie Japkowicz}, journal={Knowledge and Information Systems}, year={2008}, volume={25}, pages={1-20} }