Implementation of Feature Relevance Bounds method to perform Feature Selection and further analysis.
Feature Relevance Intervals - FRI
FRI is a Python 3 package for analytical feature selection purposes. It allows superior feature selection in the sense that all important features are conserved. At the moment we support multiple linear models for solving Classification, Regression and Ordinal Regression Problems. We also support LUPI paradigm where at learning time, privileged information is available.
Please refer to the documentation for advice. For a quick start we provide a simple guide which leads through the main functions.
FRI requires Python 3.6+.
For a stable version from
$ pip install fri
or with new versions of
pip (>=19?) you can clone the repository and run
$ pip install .
in the folder on the
Check out our online documentation here. There you can find a quick start guide and more background information.
You can also run the guide directly online without setup here.
For dependency management we use the newly released poetry tool.
If you have
poetry installed, use
$ poetry install
inside the project folder to create a new
venv and to install all dependencies.
To enter the newly created
$ poetry env
to open a new shell inside.
Or alternatively run commands inside the
poetry run ....
$ poetry run portray in_browser
to compile the files into html and launch a browser to preview changes.
(Be sure not to mix up
The documentation files are generated from
Python docstrings inside the source files
and from Markdown located in the
 Göpfert C, Pfannschmidt L, Hammer B. Feature Relevance Bounds for Linear Classification. In: Proceedings of the ESANN. 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning; https://pub.uni-bielefeld.de/publication/2908201
 Göpfert C, Pfannschmidt L, Göpfert JP, Hammer B. Interpretation of Linear Classifiers by Means of Feature Relevance Bounds. Neurocomputing. https://pub.uni-bielefeld.de/publication/2915273
 Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl, Peter Tino, Barbara Hammer: Feature Relevance Bounds for Ordinal Regression. Proceedings of the ESANN. 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning; Accepted. https://pub.uni-bielefeld.de/record/2933893
 Pfannschmidt L, Göpfert C, Neumann U, Heider D, Hammer B: FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration. Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy. https://ieeexplore.ieee.org/document/8791489
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