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Titlebook: Rough Sets, Fuzzy Sets, Data Mining and Granular Computing; 12th International C Hiroshi Sakai,Mihir Kumar Chakraborty,William Zhu Conferen

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楼主: 平凡人
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Rough Set Approximations Based on Granular Labelsuced. Lower and upper label-block approximations of sets are then defined. Properties of label-block approximation operators are also examined. Finally, relationship between properties of label-block approximation operators and some essential properties of the corresponding function is characterized
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A Logical Reasoning System of Before-after Relation Based on Bf-EVALPSNs) and applied to real-time process order control. In this paper, we introduce a logical before-after relation reasoning system based on two inference rules for before-after relation with simple examples.
发表于 2025-3-29 07:07:45 | 显示全部楼层
Dynamic Reduct from Partially Uncertain Data Using Rough Setss and is represented by the Transferable Belief Model (TBM), one interpretation of the belief function theory. To solve this problem, we propose dynamic reduct for attribute selection to extract more relevant and stable features for classification. The reduction of the uncertain decision table using
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Affordance Relationscan be based on a certain hierarchy of Pawlak’s approximation spaces. We also outline how concepts could be used in a theory of affordances, and how affordances might be recognized in simple perceiving situations.
发表于 2025-3-29 12:07:12 | 显示全部楼层
Towards an Algebraic Approach for Cover Based Rough Semantics and Combinations of Approximation Spacaces (APS) for easier algebraic semantics. .-rough set theory (RST) is also extended to accommodate local determination of universes. The results obtained are also significant in the representation theory of general granular RST, for the problems of multi source RST and Ramsey-type combinatorics.
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Discovering Concurrent Process Models in Data: A Rough Set Approachto possible applications. In particular, in Artificial Intelligence domains such as e.g. speech recognition, blind source separation and Independent Component Analysis, and also in other domains (e.g. in biology, molecular biology, finance, meteorology, etc.).
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