CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification
Run CDSMOTE-Bin.py or see demo.ipynb for a demo using the "C15" (Ecoli046_vs_5) dataset.
This dataset was obtained from:
- Cleofas-Sánchez L, Sánchez JS, García V, Valdovinos RM. Associative learning on imbalanced environments: An empirical study. Expert Syst Appl. 2016;54:387–97.
Please reference CDSMOTE as follows:
- Elyan E., Moreno-García C.F., Jayne C., CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification. Neural Comput Appl. 2020. doi:10.1007/s00521-020-05130-z
or use the following BibTex entry
@article{Elyan2020, author = {Elyan, Eyad and Moreno-Garcia, Carlos Francisco and Jayne, Chrisina}, doi = {10.1007/s00521-020-05130-z}, isbn = {0123456789}, issn = {1433-3058}, journal = {Neural Computing and Applications}, publisher = {Springer London}, title = {{CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification}}, url = {https://doi.org/10.1007/s00521-020-05130-z}, year = {2020} }