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Abstract: Although the majority of conceptlearning systems previously designed usually assume that their training sets are well-balanced, this assumption is not necessarily correct. Indeed, there exist many domains for which one class is represented by a large number of examples while the other is represented by only a few. The purpose of this paper is 1) to demonstrate experimentally that, at least in the case of connectionist systems, class imbalances hinder the performance of standard classifiers and... (Update)
Context of citations to this paper: More
...words is far greater than that of IC words. To deal with this imbalanced dataset, we have tried both down sampling [12] and over sampling [9], and found that down sampling produced more accurate classifiers than oversampling. e.g. it produced about 20 higher recall of IC...
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BibTeX entry: (Update)
Japkowicz, N. 2000. The class imbalance problem: Significance and strategies. In Proceedings of the 2000 International Conference on Artificial Intelligence (ICAI '2000). http://citeseer.ist.psu.edu/japkowicz00class.html More
@inproceedings{ japkowicz00class,
author = "Nathalie Japkowicz",
title = "The Class Imbalance Problem: Significance and Strategies",
booktitle = "Proceedings of the 2000 International Conference on Artificial Intelligence ({IC}-{AI}'2000)",
volume = "1",
pages = "111--117",
year = "2000",
url = "citeseer.ist.psu.edu/japkowicz00class.html" }
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