Neural-based classification rule learning for sequential data

Implementation of the Convolutional Rule Neural Network (CR2N) presented in "Neural-based classification rule learning for sequential data" ICLR 2023 paper [OpenReview, arXiv].

Notebooks are provided to replicate the experiments.

• main_peptides.ipynb on the UCI Anticancer dataset.
• main_synth.ipynb on synthetic datasets available here.

Complementary:

• main_synth_downsampling.ipynb - quick experiment on unbalanced dataset processed with downsampling (following reviewer's comment).

Requirements

Dependencies listed in requirements.txt. Python 3.8 was used for the experiments.

For the synthetic datasets: https://github.com/IBM/synth-sequential-datasets.

Cite

Collery, M., Bonnard, P., Fages, F., & Kusters, R. (2023). Neural-based classification rule learning for sequential data. International Conference on Learning Representations.

@inproceedings{collery2023neural,
  title={Neural-based classification rule learning for sequential data},
  author={Collery, Marine and Bonnard, Philippe and Fages, Fran{\c{c}}ois and Kusters, Remy},
  booktitle={International Conference on Learning Representations},
  year={2023}
}

License

Apache 2.0, detailed LICENSE is available here.