  In many real-world binary classification tasks (e.g. detection of certain
objects from images), an available dataset is imbalanced, i.e., it has much
less representatives of a one class (a minor class), than of another.
Generally, accurate prediction of the minor class is crucial but it's hard to
achieve since there is not much information about the minor class. One approach
to deal with this problem is to preliminarily resample the dataset, i.e., add
new elements to the dataset or remove existing ones. Resampling can be done in
various ways which raises the problem of choosing the most appropriate one. In
this paper we experimentally investigate impact of resampling on classification
accuracy, compare resampling methods and highlight key points and difficulties
of resampling.
