Traditional Discernibility Matrix-based feature selection (works on descretized dataset):

1. my_discrn_mat.py (it deals with only 26 features)
2. my_discrn_mat52.py (it deals with 52 features)

The new Fuzzy Discernibility Matrix-based can also work with continuous value dataset: my_fuzzy_discrn_mat.py (for any number of features) e.g., Irish_Evaluation_notebook.ipynb demonstrates both the feature selection techniques on standard Iris dataset.

FDM

Fuzzy Discernibility Matrix-based a novel feature selection technique

Support

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Citing

@inproceedings{chatterjee2016discernibility,
  title={Discernibility matrix based dimensionality reduction for EEG signal},
  author={Chatterjee, Rajdeep and Guha, Dibyajyoti and Sanyal, Debarshi Kumar and Mohanty, Sachi Nandan},
  booktitle={2016 IEEE Region 10 Conference (TENCON)},
  pages={2703--2706},
  year={2016},
  organization={IEEE}
}

@inproceedings{chatterjee2017dimensionality,
  title={Dimensionality reduction of EEG signal using fuzzy discernibility matrix},
  author={Chatterjee, Rajdeep and Bandyopadhyay, Tathagata and Sanyal, Debarshi Kumar and Guha, Dibyajyoti},
  booktitle={2017 10th International Conference on Human System Interactions (HSI)},
  pages={131--136},
  year={2017},
  organization={IEEE}
}

@article{chatterjee2019novel,
  title={A novel machine learning based feature selection for motor imagery EEG signal classification in Internet of medical things environment},
  author={Chatterjee, Rajdeep and Maitra, Tanmoy and Islam, SK Hafizul and Hassan, Mohammad Mehedi and Alamri, Atif and Fortino, Giancarlo},
  journal={Future Generation Computer Systems},
  volume={98},
  pages={419--434},
  year={2019},
  publisher={Elsevier}
}

License

MIT License

Copyright (c) 2021 Rajdeep Chatterjee

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