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