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LOFS: Library of Online Streaming Feature Selection
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<span title="2016-03-02">2016</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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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
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<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1603.00531v1">arXiv:1603.00531v1</a>
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... 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.
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