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Computation of Approximate Reducts with Dynamically Adjusted Approximation Threshold

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9384))

Abstract

We continue our research on dynamically adjusted approximate reducts (DAAR). We modify DAAR computation algorithm to take into account dependencies between attribute values in data. We discuss a motivation for this improvement and analyze its performance impact. We also revisit a filtering technique utilizing approximate reducts to create a ranking of attributes according to their relevance. As an illustration we study a data set from AAIA’14 Data Mining Competition.

Partially supported by Polish National Science Centre grants DEC-2012/05/B/ST6/03215 and DEC-2013/09/B/ST6/01568, and by Polish National Centre for Research and Development grants PBS2/B9/20/2013 and O ROB/0010/03/001.

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Notes

  1. 1.

    https://knowledgepit.fedcsis.org/contest/view.php?id=83.

  2. 2.

    Version 1.2.2 of the package was used in all experiments described in this paper.

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Correspondence to Dominik Ślęzak .

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Janusz, A., Ślęzak, D. (2015). Computation of Approximate Reducts with Dynamically Adjusted Approximation Threshold. In: Esposito, F., Pivert, O., Hacid, MS., Rás, Z., Ferilli, S. (eds) Foundations of Intelligent Systems. ISMIS 2015. Lecture Notes in Computer Science(), vol 9384. Springer, Cham. https://doi.org/10.1007/978-3-319-25252-0_3

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  • DOI: https://doi.org/10.1007/978-3-319-25252-0_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25251-3

  • Online ISBN: 978-3-319-25252-0

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