- RethinkNet: mlearn.models.RethinkNet
- Cost-Sensitive Reference Pair Encoding (CSRPE): mlearn.models.CSRPE
- Probabilistic Classifier Chains: mlearn.models.ProbabilisticClassifierChains
- Binary Relevance: mlearn.models.BinaryRelevance
- Classifier Chains: mlearn.models.ClassifierChains
- RAndom K labELsets: mlearn.models.RandomKLabelsets
Compile and install the C-extensions
python ./setup.py install
Run example locally
pip install numpy Cython
python ./setup.py build_ext -i
PYTHONPATH=. python ./examples/classification.py
If you use some of my works in a scientific publication, we would appreciate citations to the following papers:
For RethinkNet, please cite
@article{yang2018deep,
title={Deep learning with a rethinking structure for multi-label classification},
author={Yang, Yao-Yuan and Lin, Yi-An and Chu, Hong-Min and Lin, Hsuan-Tien},
journal={arXiv preprint arXiv:1802.01697},
year={2018}
}
For Cost-Sensitive Reference Pair Encoding (CSRPE), please cite
@inproceedings{YY2018csrpe,
title = {Cost-Sensitive Reference Pair Encoding for Multi-Label Learning},
author = {Yao-Yuan Yang and Kuan-Hao Huang and Chih-Wei Chang and Hsuan-Tien Lin},
booktitle = {Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
year = 2018,
arxiv = {https://arxiv.org/abs/1611.09461},
software = {https://github.com/yangarbiter/multilabel-learn/blob/master/mlearn/models/csrpe.py},
}