Feature Selection System

A Feature Selection System is an dimensionality reduction system that can solve a feature selection task by implementing a feature selection algorithm.

• AKA: Feature Subset Selection System.
• Context:
  • It can range from being an Unsupervised Feature Selection System to being a Supervised Feature Selection System.
  • …
• Example(s):
  • a scikit-feature.
  • an Unsupervised Feature Selection System, such as a Laplacian Score System, or a Frequency-based Feature Selection System.
  • a Supervised Feature Selection System, such as a RELIEF System.
  • …
• Counter-Example(s):
  • a Feature Dimensionality Reduction System.
• See: Feature Space.

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References

2019

• (Feature Selection, 2016) ⇒ http://featureselection.asu.edu/index.php Retrieved: 2019-07-27.
  • QUOTE: 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.

2016

• (Feature Selection, 2016) ⇒ http://featureselection.asu.edu/algorithms.php
  • QUOTE: The feature selection repository is designed to collect some widely used feature selection algorithms that have been developed in the feature selection research to serve as a platform for facilitating their application, comparison and joint study. The feature selection repository also effectively assists researchers to achieve more reliable evaluation in the process of developing new feature selection algorithms. We develop the open source feature selection repository scikit-feature by one of the most popular programming language - python. It contains more than 40 popular feature selection algorithms, including most traditional feature selection algorithms and some structural and streaming feature selection algorithms. It is built upon one widely used machine learning package Scikit-learn and two scientific computing packages Numpy and Scipy.

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