Boosting support vector machines for imbalanced data sets

Abstract

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

Topics

4 Figures and Tables

Statistics

0502009201020112012201320142015201620172018
Citations per Year

356 Citations

Semantic Scholar estimates that this publication has 356 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@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} }