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evofs
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.. |Build| image:: https://img.shields.io/travis/pietrobarbiero/evofs?label=Master%20Build&style=for-the-badge :alt: Travis (.org) :target: https://travis-ci.org/pietrobarbiero/evofs
.. |Coverage| image:: https://img.shields.io/codecov/c/gh/pietrobarbiero/evofs?label=Test%20Coverage&style=for-the-badge :alt: Codecov :target: https://codecov.io/gh/pietrobarbiero/evofs
.. |PyPI license| image:: https://img.shields.io/pypi/l/evofs.svg?style=for-the-badge :target: https://pypi.python.org/pypi/evofs/
.. |PyPI-version| image:: https://img.shields.io/pypi/v/evofs?style=for-the-badge :alt: PyPI :target: https://pypi.python.org/pypi/evofs/
EvoFS is a python package providing a sklearn-like transformer for multi-objective evolutionary feature selection.
You can install EvoFS along with all its dependencies from
PyPI <https://pypi.org/project/evofs/>__:
.. code:: bash
$ pip install evofs
If you find EovFS useful in your research, please consider citing the following paper::
@inproceedings{barbiero2019novel,
title={A Novel Outlook on Feature Selection as a Multi-objective Problem},
author={Barbiero, Pietro and Lutton, Evelyne and Squillero, Giovanni and Tonda, Alberto},
booktitle={International Conference on Artificial Evolution (Evolution Artificielle)},
pages={68--81},
year={2019},
organization={Springer}
}
The source code and minimal working examples can be found on
GitHub <https://github.com/pietrobarbiero/moea-feature-selection>__.
You can run all unittests from command line by using python:
.. code:: bash
$ python -m unittest discover
or coverage:
.. code:: bash
$ coverage run -m unittest discover
Pietro Barbiero <http://www.pietrobarbiero.eu/>,
Giovanni Squillero <https://staff.polito.it/giovanni.squillero/>,
and
Alberto Tonda <https://www.researchgate.net/profile/Alberto_Tonda>__.
Copyright 2020 Pietro Barbiero, Giovanni Squillero, and Alberto Tonda.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0.
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and limitations under the License.
FAQs
Multi-objective evolutionary feature selection.
We found that evofs demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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