Metadata-Version: 2.1
Name: niaclass
Version: 0.1.2
Summary: Python framework for building classifiers using nature-inspired algorithms.
Home-page: https://github.com/lukapecnik/NiaClass
License: MIT
Keywords: classification,NiaPy,nature-inspired algorithms
Author: Luka Pečnik
Author-email: lukapecnik96@gmail.com
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Dist: NiaPy
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Project-URL: Repository, https://github.com/lukapecnik/NiaClass
Description-Content-Type: text/x-rst

NiaClass
========

NiaClass is a framework for solving classification tasks using nature-inspired algorithms. The framework is written fully in Python. Its goal is to find the best possible set of classification rules for the input data using the `NiaPy framework <https://github.com/NiaOrg/NiaPy>`_, which is a popular Python collection of nature-inspired algorithms. The NiaClass classifier support numerical and categorical features.

License
-------

This package is distributed under the MIT License. This license can be
found online at http://www.opensource.org/licenses/MIT.

Disclaimer
----------

This framework is provided as-is, and there are no guarantees that it
fits your purposes or that it is bug-free. Use it at your own risk!

References
----------

[1] Iztok Fister Jr., Iztok Fister, Dušan Fister, Grega Vrbančič, Vili Podgorelec. On the potential of the nature-inspired algorithms for pure binary classification. In. Computational science - ICCS 2020 : 20th International Conference, Proceedings. Part V. Cham: Springer, pp. 18-28. Lecture notes in computer science, 12141, 2020
