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A python package for inducing membership functions from labeled data

Project description

mulearn

Documentation Status

A python package for inducing membership functions from labeled data

mulearn is a python package implementing the metodology for data-driven induction of fuzzy sets described in

  • D. Malchiodi and W. Pedrycz, Learning Membership Functions for Fuzzy Sets through Modified Support Vector Clustering, in F. Masulli, G. Pasi e R. Yager (Eds.), Fuzzy Logic and Applications. 10th International Workshop, WILF 2013, Genoa, Italy, November 19–22, 2013. Proceedings., Vol. 8256, Springer International Publishing, Switzerland, Lecture Notes on Artificial Intelligence, 2013;
  • D. Malchiodi and A. G. B. Tettamanzi, Predicting the Possibilistic Score of OWL Axioms through Modified Support Vector Clustering, in H. Haddad, R. L. Wainwright e R. Chbeir (Eds.), SAC'18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing, ACM (ISBN 9781450351911), 1984–1991, 2018.

Install

The package can easily be installed:

  • via pip, by running pip install mulearn in a terminal;
  • through conda, by running conda install -c dariomalchiodi mulearn;
  • cloning this repo.

APIs are described at https://mulearn.readthedocs.io/.

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