- 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 installRun example locally
pip install numpy Cython python ./setup.py build_ext -i PYTHONPATH=. python ./examples/classification.pyIf 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}, }