Metadata-Version: 2.1
Name: InformativeFeatureSelection
Version: 3.0.1
Summary: Package which provides an feature selection algorithm which considers class separability and an implementation of Informative Normalized Difference Index (INDI)
Home-page: https://gitlab.com/rustam-industries/feature_extractor
Author: Mukhin Artem
Author-email: artemmukhinssau@gmail.com
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy ~=1.21
Requires-Dist: scikit-learn ~=0.24.2
Requires-Dist: setuptools ~=58.0.4
Requires-Dist: opencv-python-headless ~=4.5.3.56
Requires-Dist: numba ~=0.57.1
Provides-Extra: smart
Requires-Dist: segment-anything ==1.0 ; extra == 'smart'

# Install

`pip install InformativeFeatureSelection`

# Features

* Several implementations of feature selection algorithms based on discriminant analysis
* Binary implementation of Informative Normalized Difference Index (INDI)
* Multiclass implementation of INDI 

INDI may be extremely usefully in hyperspectral imaging analysis.

Implemented algorithms were proposed in the following papers:
1. [Paringer RA, Mukhin AV, Kupriyanov AV. Formation of an informative index for recognizing specified 
objects in hyperspectral data. Computer Optics 2021; 45(6): 873-878. DOI: 10.18287/2412-6179-CO-930.](http://www.computeroptics.ru/KO/PDF/KO45-6/450611.pdf)

2. [Mukhin, A., Paringer, R. and Ilyasova, N., 2021, September. Feature selection algorithm with feature space
separability estimation using discriminant analysis. In 2021 International Conference on Information Technology
and Nanotechnology (ITNT) (pp. 1-4). IEEE.](https://ieeexplore.ieee.org/document/9649144)

# Usage example

See jupyter notebook file in `examples` folder. 

# License

[MIT License](LICENSE)
