ClassImbalanceLearning

This is the implementation code for the paper "Trainable Undersampling for Class-Imbalance Learning" published in AAAI2019

File Description

utils.py:

load dataset for given task; define some evaluation functions.

trainer.py:

implement the policy; perform model training.

adaptive_trainer.py:

similar to trainer.py, but trains the policy on gradually increasing data set.

synthetic.ipynb:

generate synthetic data; choose supervised classifier and its corresponding hyper-parameters; get results reported in Table 1 of the paper.

checkerboard.ipynb:

choose supervised classifier and its corresponding hyper-parameters; plot classification boundaries on original dataset and the sampled dataset with our proposed method as reported in Figure 1 of the paper.

page.ipynb:

choose supervised classifier and its corresponding hyper-parameters on page dataset; apply typical data sampling methods to this dataset and the chosen classifier.

spam.ipynb:

similar to page.ipynb but performs on the spam message dataset.

vehicle.ipynb:

similar to page.ipynb but performs on the vehicle dataset.

vehicle.ipynb:

similar to page.ipynb but performs on the creditcard dataset.