Deep Similarity Learning Loss Functions in Data Transformation for Class
  Imbalance
Damian Horna Lango Mateusz Jerzy Stefanowski
Improving the classification of multi-class imbalanced data is more difficult
than its two-class counterpart. In this paper, we use deep neural networks to
train new representations of tabular multi-class data. Unlike the typically
developed re-sampling pre-processing methods, our proposal modifies the
distribution of features, i.e. the positions of examples in the learned
embedded representation, and it does not modify the class sizes. To learn such
embedded representations we introduced various definitions of triplet loss
functions: the simplest one uses weights related to the degree of class
imbalance, while the next proposals are intended for more complex distributions
of examples and aim to generate a safe neighborhood of minority examples.
Similarly to the resampling approaches, after applying such preprocessing,
different classifiers can be trained on new representations. Experiments with
popular multi-class imbalanced benchmark data sets and three classifiers showed
the advantage of the proposed approach over popular pre-processing methods as
well as basic versions of neural networks with classical loss function
formulations.