This project contains a novel feature selection algorithm which performs feature classification and selection using a Random Forest classifier (LightGBM) and Boruta.

It classifies each data feature into the three classes (1) strong relevant features (2) weakly relevant features (3) irrelevant features.

A Publication detailing the methods used here is WIP.

The name is a codename without meaning and chosen because of personal reasons (Beautiful British Columbia...)

We use poetry as our dependency manager and packaging tool.

poetry install

poetry run pytest

@misc{pfannschmidt2020sequential,
    title={Sequential Feature Classification in the Context of Redundancies},
    author={Lukas Pfannschmidt and Barbara Hammer},
    year={2020},
    eprint={2004.00658},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

Preprints can be found at https://pub.uni-bielefeld.de/record/2942271 or https://arxiv.org/abs/2004.00658. Experiments of the papers are located here.