Abstract
In this paper we overview two feature rankings methods that utilize basic notions from the rough set theory, such as the idea of the decision reducts. We also propose a new algorithm, called Rough Attribute Ranker. In our approach, the usefulness of features is measured by their impact on quality of the reducts that contain them. We experimentally compare the reduct-based methods with several classic attribute rankers using synthetic, as well as real-life high dimensional datasets.
The authors are supported by the grant N N516 077837 from the Ministry of Science and Higher Education of the Republic of Poland and by the National Centre for Research and Development (NCBiR) under Grant No. SP/I/1/77065/10 by the strategic scientific research and experimental development program: “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.
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Janusz, A., Stawicki, S. (2011). Applications of Approximate Reducts to the Feature Selection Problem. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds) Rough Sets and Knowledge Technology. RSKT 2011. Lecture Notes in Computer Science(), vol 6954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24425-4_8
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DOI: https://doi.org/10.1007/978-3-642-24425-4_8
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