Feature selection repository scikit-feature in Python

J. Li, K. Cheng, S. Wang, F. Morstatter, R.P. Trevino, J. Tang, and H. Liu, “Feature Selection: A Data Perspective,” ACM Computing Surveys, vol. 50, no. 6, pp. 1–45, December 2017. https://doi.org/10.1145/3136625 (Python at GitHub)

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.