Discussion of other data-level deep learning methods and their key ideas
In addition to the methods previously discussed, there is a rich array of other techniques specifically designed to address imbalanced data challenges. This section provides a high-level overview of these alternative approaches, each offering unique insights and potential advantages. While we will only touch upon their key ideas, we encourage you to delve deeper into the literature and explore them further if you find these techniques intriguing.
Two-phase learning
Two-phase learning [16][17] is a technique designed to enhance the performance of minority classes in multi-class classification problems, without compromising the performance of majority classes. The process involves two training phases:
- In the first phase, a deep learning model is first trained on the dataset, which is balanced with respect to each class. Balancing can be done using sampling techniques such as random oversampling or...
