Experimental Perspectives on Learning from Imbalanced Data
Abstract
We present a comprehensive suite of experimentation on the sub ject of learning from imbalanced data. When classes are imbalanced, many learning algorithms can suffer from the perspective of reduced performance. Can data sampling be used to improve the performance of learners built from imbalanced data? Is the effectiveness of sampling related to the type of learner? Do the results change if the ob jective is to optimize different performance metrics? We address these and other issues in this work, showing that sampling in many cases will improve classifier performance.
Cite
Text
Van Hulse et al. "Experimental Perspectives on Learning from Imbalanced Data." International Conference on Machine Learning, 2007. doi:10.1145/1273496.1273614Markdown
[Van Hulse et al. "Experimental Perspectives on Learning from Imbalanced Data." International Conference on Machine Learning, 2007.](https://mlanthology.org/icml/2007/hulse2007icml-experimental/) doi:10.1145/1273496.1273614BibTeX
@inproceedings{hulse2007icml-experimental,
title = {{Experimental Perspectives on Learning from Imbalanced Data}},
author = {Van Hulse, Jason and Khoshgoftaar, Taghi M. and Napolitano, Amri},
booktitle = {International Conference on Machine Learning},
year = {2007},
pages = {935-942},
doi = {10.1145/1273496.1273614},
url = {https://mlanthology.org/icml/2007/hulse2007icml-experimental/}
}