Feature relevance intervals
This repository contains the Python implementation of the Feature Relevance Intervals method (FRI)[1,2].
The library needs various dependencies which should automatically be installed. We highly recommend the Anaconda Python distribution to provide all dependencies. The library was written with Python 3 in mind and due to the foreseeable ending of Python 2 support, backwards compatibility is not planned.
If you just want to use the stable version from PyPI use
$ pip install fri
To install the module in development clone the repo and execute:
$ python setup.py install
To test if the library was installed correctly you can use the
pytest command to run all included tests.
$ pip install pytest
then run in the root directory:
Examples and API descriptions can be found here.
 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