  Class imbalance, which is also called long-tailed distribution, is a common
problem in classification tasks based on machine learning. If it happens, the
minority data will be overwhelmed by the majority, which presents quite a
challenge for data science. To address the class imbalance problem, researchers
have proposed lots of methods: some people make the data set balanced (SMOTE),
some others refine the loss function (Focal Loss), and even someone has noticed
the value of labels influences class-imbalanced learning (Yang and Xu.
Rethinking the value of labels for improving class-imbalanced learning. In
NeurIPS 2020), but no one changes the way to encode the labels of data yet.
Nowadays, the most prevailing technique to encode labels is the one-hot
encoding due to its nice performance in the general situation. However, it is
not a good choice for imbalanced data, because the classifier will treat
majority and minority samples equally. In this paper, we innovatively propose
the enhancement encoding technique, which is specially designed for the
imbalanced classification. The enhancement encoding combines re-weighting and
cost-sensitiveness, which can reflect the difference between hard and easy (or
minority and majority) classes. To reduce the number of validation samples and
the computation cost, we also replace the confusion matrix with a novel
soft-confusion matrix which works better with a small validation set. In the
experiments, we evaluate the enhancement encoding with three different types of
loss. And the results show that enhancement encoding is very effective to
improve the performance of the network trained with imbalanced data.
Particularly, the performance on minority classes is much better.
