Hybrid Loss for Improving Classification Performance with Unbalanced Data

Abstract

Unbalanced data is widespread in practice and presents challenges which have been widely studied in classical machine learning. A classification algorithm trained with unbalanced data is likely to be biased towards the majority class and thus show inferior performance on the minority class. To improve the performance of deep neural network (DNN) models on poorly balanced data, we hybridized two well-performing loss functions, specially designed for learning imbalanced data, mean false error and focal loss. Since mean false error can effectively balance between majority and minority classes and focal loss can reduce the contribution of unnecessary samples, which are usually samples from the majority class, which may cause a DNN model to be biased towards the majority class when learning. We show that hybridizing the two losses can improve the classification performance of the model. Our hybrid loss function was tested with unbalanced data sets, extracted from CIFAR-100 and IMDB review datasets, and showed that, overall, it performed better than mean false error or focal loss.