For users who are still using our previous feature selection repository implemented in Matlab, please find the old project webpage here
About
scikit-feature is an open-source feature selection repository in Python developed at Arizona State University. It is built upon one widely used machine learning package scikit-learn and two scientific computing packages Numpy and Scipy. scikit-feature contains around 40 popular feature selection algorithms, including traditional feature selection algorithms and some structural and streaming feature selection algorithms. It serves as a platform for facilitating feature selection application, research and comparative study. It is designed to share widely used feature selection algorithms developed in the feature selection research, and offer convenience for researchers and practitioners to perform empirical evaluation in developing new feature selection algorithms.
Installing scikit-feature
Prerequisites:
Steps:
Source code is available on GitHub.
For Linux users, you can install the repository by the following command:
python setup.py installFor Windows users, you can also install the repository by the following command:
setup.py installFor scikit-feature API usage, please refer scikit-feature feature selection repository API Document.
A brief introduction on how to perform feature selection with the scikit-feature repository scikit-feature feature selection tutorial.
Citation
If you find scikit-feature feature selection repository useful in your research, please consider cite the following paper [pdf] :
@article{li2018feature,
title={Feature selection: A data perspective},
author={Li, Jundong and Cheng, Kewei and Wang, Suhang and Morstatter, Fred and Trevino, Robert P and Tang, Jiliang and Liu, Huan},
journal={ACM Computing Surveys (CSUR)},
volume={50},
number={6},
pages={94},
year={2018},
publisher={ACM}
}Contact
Jundong Li
E-mail: jundong.li@asu.edu
Kewei Cheng
E-mail: kcheng18@asu.edu
Kaize Ding
E-mail: kding9@asu.edu
Faisal Alatawi
Huan Liu
E-mail: huan.liu@asu.edu