  In many application domains such as medicine, information retrieval,
cybersecurity, social media, etc., datasets used for inducing classification
models often have an unequal distribution of the instances of each class. This
situation, known as imbalanced data classification, causes low predictive
performance for the minority class examples. Thus, the prediction model is
unreliable although the overall model accuracy can be acceptable. Oversampling
and undersampling techniques are well-known strategies to deal with this
problem by balancing the number of examples of each class. However, their
effectiveness depends on several factors mainly related to data intrinsic
characteristics, such as imbalance ratio, dataset size and dimensionality,
overlapping between classes or borderline examples. In this work, the impact of
these factors is analyzed through a comprehensive comparative study involving
40 datasets from different application areas. The objective is to obtain models
for automatic selection of the best resampling strategy for any dataset based
on its characteristics. These models allow us to check several factors
simultaneously considering a wide range of values since they are induced from
very varied datasets that cover a broad spectrum of conditions. This differs
from most studies that focus on the individual analysis of the characteristics
or cover a small range of values. In addition, the study encompasses both basic
and advanced resampling strategies that are evaluated by means of eight
different performance metrics, including new measures specifically designed for
imbalanced data classification. The general nature of the proposal allows the
choice of the most appropriate method regardless of the domain, avoiding the
search for special purpose techniques that could be valid for the target data.
