LOFS: A library of online streaming feature selection

摘要

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, called LOFS, for use in MATLAB and OCTAVE 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 research direction and make comparisons between the new methods and existing ones available. LOFS is available from https://github.com/kuiy/LOFS.