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Minimal Attribute Space Bias for Attribute Reduction

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Rough Sets and Knowledge Technology (RSKT 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4481))

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Abstract

Attribute reduction is an important inductive learning issue addressed by the Rough Sets society. Most existing works on this issue use the minimal attribute bias, i.e., searching for reducts with the minimal number of attributes. But this bias does not work well for datasets where different attributes have different sizes of domains. In this paper, we propose a more reasonable strategy called the minimal attribute space bias, i.e., searching for reducts with the minimal attribute domain sizes product. In most cases, this bias can help to obtain reduced decision tables with the best space coverage, thus helpful for obtaining small rule sets with good predicting performance. Empirical study on some datasets validates our analysis.

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JingTao Yao Pawan Lingras Wei-Zhi Wu Marcin Szczuka Nick J. Cercone Dominik Ślȩzak

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© 2007 Springer Berlin Heidelberg

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Min, F., Du, X., Qiu, H., Liu, Q. (2007). Minimal Attribute Space Bias for Attribute Reduction. In: Yao, J., Lingras, P., Wu, WZ., Szczuka, M., Cercone, N.J., Ślȩzak, D. (eds) Rough Sets and Knowledge Technology. RSKT 2007. Lecture Notes in Computer Science(), vol 4481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72458-2_47

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  • DOI: https://doi.org/10.1007/978-3-540-72458-2_47

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72457-5

  • Online ISBN: 978-3-540-72458-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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