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RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
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RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
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RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
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Nearness of Objects: Extension of Approximation Space Model
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Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
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The presentation introduces the basic ideas and investigates the stochastic approach to rough set theory. The major aspects of the stochastic approach to rough set theory to be explored during the presentation are: the probabilistic view of the approximation space, the probabilistic approximations of sets, as expressed via variable precision and Bayesian rough set models, and probabilistic dependencies between sets and multi-valued attributes, as expressed by the absolute certainty gain and expected certainty gain measures, respectively. The measures allow for more comprehensive evaluation of rules computed from data and for computation of attribute reduct, core and significance factors in probabilistic decision tables.