Implement CFS Per Group

[simonprovost]

Title: Implement the CFS Per Group Feature Selection's Technique 🔬

Description: Implement the CFS Per Group Algorithm which should handle both standard and longitudinal data. I.e, for CFS without per group apply the standard method and for the per group variation apply the algorithm considering the features waves group 🚀

📚Reference:
Pomsuwan, T. and Freitas, A.A., 2017, November. Feature selection for the classification of longitudinal human ageing data. In 2017 IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 739-746). IEEE.

🎯 Task Breakdown:

• 1. Implement, improve and parralelize an original CFS algorithm's python implementation
  • Description: Follow * Zixiao. S. (2019, August 11). GitHub - ZixiaoShen Correlation-based-Feature-Selection, available at:
    https://github.com/ZixiaoShen/Correlation-based-Feature-Selection
• 2. Improve the implementation to accept the per group variation of the CFS, considering features waves
• 3. Create unit tests for each implementation 🧪
  • Description: Develop comprehensive unit tests to validate the correct implementation and functionality of each variation (standard, per group) implemented.

🎉 Cheers! 🎉