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.

翻译：改进多类别不平衡数据的分类比二类别情况更为困难。本文利用深度神经网络训练表格多类别数据的新表示。与通常开发的再采样预处理方法不同，我们的方法修改特征的分布，即学习嵌入表示中样本的位置，而不调整类别规模。为学习此类嵌入表示，我们引入了多种三元组损失函数的定义：最简单的定义使用与类别不平衡程度相关的权重，而后续的定义则针对更复杂的样本分布，旨在为少数类样本生成安全邻域。与再采样方法类似，应用此类预处理后，可在新表示上训练不同的分类器。在多类别不平衡基准数据集及三种分类器上的实验表明，所提出方法优于流行的预处理方法以及采用传统损失函数表述的基本神经网络版本。