Implementation of Deep reinforcement learning for imbalanced classification and its extended version to multi-class imbalanced classification.
- Double DQN and Dueling DQN are applied.
- The reward function on the paper is extended to multi-class imbalanced data.
- It has been implemented to easily test various multi-class imbalanced settings of Cifar-10 dataset.
- Python 3.7
- Tensorflow 1.14
You can check example codes for some major configurations in demo.sh
.
$ ./demo.sh
The values of train parameters from the original paper are used.
Dataset | Imbalance ratio | F-measure |
---|---|---|
Cifar-10(1) | 4% | 0.901 |
2% | 0.879 | |
1% | 0.862 | |
0.5% | 0.784 | |
Cifar-10(2) | 4% | 0.887 |
2% | 0.855 | |
1% | 0.806 | |
0.5% | 0.708 |