On the Existence of a Threshold in Class Imbalance Problems

One common approach to class imbalance problem is the resampling of data. However this strategy has some drawbacks, e.g., Unnecessary noise or the possibility of throwing out useful information. These inconveniences may be avoided or minimized by using a class proportion threshold allowing to identify when the imbalance data represent a problem to the classifier performance. In this paper we investigate the existence of this threshold and evaluate the performance of different classifiers in imbalanced problems. Results showed that for classifiers sensible to imbalanced data this threshold exists.