Abstract
•We designed and implemented a novel sampling scheme based on monotonic chains in order to preverse monotonicity while obtaining a balanced set.•This scheme has been included in five famous under- and over-sampling approaches: Random Under-Sampling, Random Over-Sampling, SMOTE, ADASYN and MWMOTE.•Empirically, we show the deterioration of the monotonicity degree in 8 data-sets caused by standard and ordinal sampling approaches, invalidating their use for monotonic classification.•We compare our new monotonic sampling techniques versus their standard versions, achieving a better monotonicity preservation than the standard ones with the same improvement of predictability.
Classification with monotonic constraints arises from some ordinal real-life problems. In these real-life problems, it is common to find a big difference in the number of instances representing middle-ranked classes and the top classes, because the former usually represents the average or the normality, while the latter are the exceptional and uncommon. This is known as class imbalance problem, and it deteriorates the learning of those under-represented classes. However, the traditional solutions cannot be applied to applications that require monotonic restrictions to be asserted. Since these were not designed to consider monotonic constraints, they compromise the monotonicity of the data-sets and the performance of the monotonic classifiers. In this paper, we propose a set of new sampling techniques to mitigate the imbalanced class distribution and, at the same time, maintain the monotonicity of the data-sets. These methods perform the sampling inside monotonic chains, sets of comparable instances, in order to preserve them and, as a result, the monotonicity. Five different approaches are redesigned based on famous under- and over-sampling techniques and their standard and ordinal versions are compared with outstanding results.