Index page

Class Imbalance Problem

[15] Class Imbalance Problem - Buda, M., Maki, A., & Mazurowski, M. A. (2018). A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks, 106, 249-259. [Paper](https://arxiv.org/pdf/1710.053

Notes

• the effect of class imbalance on classification performance is detrimental
• the method of addressing class imbalance that emerged as dominant in almost all analyzed scenarios was oversampling
• oversampling does not cause overfitting of CNN

• thresholding should be applied to compensate for prior class probabilities when overall number of properly classified cases is of interest.

• oversampling

• random minority oversampling, which simply replicates randomly selected samples from minority classes.
• Cluster-based oversampling first clusters the dataset and then oversamples each cluster separately
• An oversampling approach specific to neural networks optimized with stochastic gradient descent is class aware sampling. The main idea is to ensure uniform class distribution of each mini-batch and control the selection of examples from each class.
• Thresholding
• It is applied in the test phase and involves changing the output class probabilities. There are many ways in which the network outputs can be adjusted
• Cost sensitive learning.
• This method assigns different cost to misclassification of examples from different classes
• One-class classification.
• In the context of neural networks it is usually called novelty detection. This is a concept learning technique that recognizes positive instances rather than discriminating between two classes. Autoencoders used for this purpose are trained to perform autoassociative mapping, i.e. identity function
• effect of imbalance is significantly stronger for the task with higher complexity
• Undersampling showed a generally poor performance.
• not only the total number of examples matters but also its distribution between classes.
• thresholding worked particularly well when applied jointly with oversampling.