Feature selection repository scikit-feature in Python (DMML Lab@ASU).
scikit-feature is an open-source feature selection repository in Python developed by Data Mining and Machine Learning Lab 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.
Python 2.7 and Python 3
NumPy
SciPy
Scikit-learn
For Linux users, you can install the repository by the following command:
python setup.py install
For Windows users, you can also install the repository by the following command:
setup.py install
Instructions of using this repository can be found in our project webpage at http://featureselection.asu.edu/
If you find scikit-feature feature selection reposoitory useful in your research, please consider citing the following paper::
@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} }
Jundong Li E-mail: [email protected]
Kewei Cheng E-mail: [email protected]