Title: Meta-Learning for Resampling Recommendation Systems,
Abstract: One possible approach to tackle the class imbalance in classification tasks
is to resample a training dataset, i.e., to drop some of its elements or to
synthesize new ones. There exist several widely-used resampling methods. Recent
research showed that the choice of resampling method significantly affects the
quality of classification, which raises resampling selection problem.
Exhaustive search for optimal resampling is time-consuming and hence it is of
limited use. In this paper, we describe an alternative approach to the
resampling selection. We follow the meta-learning concept to build resampling
recommendation systems, i.e., algorithms recommending resampling for datasets
on the basis of their properties.