Optimizing Classifers for Imbalanced Training Sets
Abstract
Following recent results [9, 8] showing the importance of the fat(cid:173) shattering dimension in explaining the beneficial effect of a large margin on generalization performance, the current paper investi(cid:173) gates the implications of these results for the case of imbalanced datasets and develops two approaches to setting the threshold. The approaches are incorporated into ThetaBoost, a boosting al(cid:173) gorithm for dealing with unequal loss functions. The performance of ThetaBoost and the two approaches are tested experimentally.
Cite
Text
Karakoulas and Shawe-Taylor. "Optimizing Classifers for Imbalanced Training Sets." Neural Information Processing Systems, 1998.Markdown
[Karakoulas and Shawe-Taylor. "Optimizing Classifers for Imbalanced Training Sets." Neural Information Processing Systems, 1998.](https://mlanthology.org/neurips/1998/karakoulas1998neurips-optimizing/)BibTeX
@inproceedings{karakoulas1998neurips-optimizing,
title = {{Optimizing Classifers for Imbalanced Training Sets}},
author = {Karakoulas, Grigoris I. and Shawe-Taylor, John},
booktitle = {Neural Information Processing Systems},
year = {1998},
pages = {253-259},
url = {https://mlanthology.org/neurips/1998/karakoulas1998neurips-optimizing/}
}