Abstract
We discuss applications of random probes in a process of computation and assessment of approximate reducts. By random probes we mean artificial attributes, generated independently from a decision vector but having similar value distributions to the attributes in the original data. We introduce a concept of a randomized reduct which is a reduct constructed solely from random probes and we show how to use it for unsupervised evaluation of attribute sets. We also propose a modification of the greedy heuristic for a computation of approximate reducts, which reduces a chance of including irrelevant attributes into a reduct. To support our claims we present results of experiments on high dimensional data. Analysis of obtained results confirms usefulness of random probes in a search for informative attribute sets.
Partly supported by Polish National Science Centre (NCN) grants DEC-2011/01/B/ST6/03867 and DEC-2012/05/B/ST6/03215, and by National Centre for Research and Development (NCBiR) grant 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., Ślęzak, D. (2014). Random Probes in Computation and Assessment of Approximate Reducts. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds) Rough Sets and Intelligent Systems Paradigms. Lecture Notes in Computer Science(), vol 8537. Springer, Cham. https://doi.org/10.1007/978-3-319-08729-0_5
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DOI: https://doi.org/10.1007/978-3-319-08729-0_5
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