Celestine is a Python-based procedure to solve classification problems through five Machine Learning methods:
- K-NN
- SVM
- Naive Bayes
- Random Forest
- Convolutional Neural Network (CNN)
Celestine requires Python 3. It also depends on the following Python packages:
Celestine is fully documented in its github-pages. You can also generate its
docs from the source code. Simply change directory to the doc subfolder and type in
make html, the documentation will be under build/html. You will need
Sphinx to build the documentation.
There are two files to perform the classification:
cnn.py- Script to run only the CNN.classfiers.py- Script to run the rest of classifiers.
The command to execute each script is as follows:
$ python3 script data_train.npy labels_train.npy data_test.npy labels_test.npy MRMR.csv
where:
- script is
cnn.pyorclassifiers.py. - data_train.npy is the file with the training dataset data (in .npy format).
- labels_train.npy is the file with the training dataset labels (in .npy format).
- data_test.npy is the file with the test dataset data (in .npy format).
- labels_test.npy is the file with the test dataset labels (in .npy format).
- MRMR.csv is the file with mRMR features ranking (in .csv format).
Finally, once the script is finished, the accuracy of each method will be saved in a local database using the PyMongo library.
- J. C. Gómez-López, J. J. Escobar, J. González, F. Gil-Montoya, J. Ortega, M. Burmester, M. Damas. Energy-Time Profiling for Machine Learning Methods to EEG Classification. In: International Conference on Bioengineering and Biomedical Signal and Image Processing. BIOMESIP 2021, pp. 311-322. https://doi.org/10.1007/978-3-030-88163-4_27
This work was supported by project New Computing Paradigms and Heterogeneous Parallel Architectures for High-Performance and Energy Efficiency of Classification and Optimization Tasks on Biomedical Engineering Applications (HPEE-COBE), with reference PGC2018-098813-B-C31, funded by the Spanish Ministerio de Ciencia, Innovación y Universidades, and by the European Regional Development Fund (ERDF).
Celestine © 2015 EFFICOMP.