MACFE

Meta-learning and Causality Based Feature Engineering

Paper: https://link.springer.com/chapter/10.1007/978-3-031-19493-1_5

Instructions

1. Original datasets to transform should be on datasets_input/ folder. There is inside an example of dataset (sonar).
2. To make use of MACFE in a GridSearch fashion, run the file run.py.
3. Output datasets transformed are on datasets_output/ folder
4. A file results.csv will be generated with the evaluation results of F1-score, Accuracy and AUC for eight classifiers (KNN, LR, SVC-L, SVC-P, RF, AB, MLP, DT)

Cite this work:

@InProceedings{10.1007/978-3-031-19493-1_5, author="Reyes-Amezcua, Ivan", title="MACFE: A Meta-learning and Causality Based Feature Engineering Framework", booktitle="Advances in Computational Intelligence", year="2022", publisher="Springer Nature Switzerland", pages="52--65", isbn="978-3-031-19493-1" }