LOFS: Library of Online Streaming Feature Selection
This work provides a tool for researchers to develop and compare algorithms in online streaming feature selection, but it is incremental as it packages existing methods into a library without introducing new algorithmic advancements.
The authors introduced LOFS, the first comprehensive open-source MATLAB library implementing state-of-the-art algorithms for online streaming feature selection, which handles sequentially added features with fixed data instances to address high dimensionality in big data analytics.
As an emerging research direction, online streaming feature selection deals with sequentially added dimensions in a feature space while the number of data instances is fixed. Online streaming feature selection provides a new, complementary algorithmic methodology to enrich online feature selection, especially targets to high dimensionality in big data analytics. This paper introduces the first comprehensive open-source library for use in MATLAB that implements the state-of-the-art algorithms of online streaming feature selection. The library is designed to facilitate the development of new algorithms in this exciting research direction and make comparisons between the new methods and existing ones available.