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Class imbalance problems have lately become an important area of study in machine learning and are often solved using intelligent resampling methods to balance the class distribution. The aim of this work is to show that balancing the class distribution is not always the best solution when intelligent resampling methods are used, i.e. there is often a class distribution other than 50 % that improves the results. This paper presents a methodology to find a (near-)optimal class distribution. Experiments have been performed using 29 databases, 8 different resampling methods and two learning algorithms, and have been evaluated using AUC performance metric and statistical tests. The results go beyond the original objective, because they show that although intelligent resampling methods are mostly used to solve class imbalance problems they can also be used to improve results, using the class distribution found in data sets that are not imbalanced. As a consequence, we can state that when solving any machine learning problem it is always worth looking for the optimal class distribution.

The quest for the optimal class distribution: an approach for enhancing the effectiveness of learning via resampling methods for imbalanced data sets