The Class Imbalance Problem: Signiicance and Strategies
نویسنده
چکیده
Although the majority of concept-learning 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 clas-siiers and 2) to compare the performance of several approaches previously proposed to deal with the problem.
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