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A first-order, four valued, weakly paraconsistent logic and its relation to rough sets semantics
, 2002 Alexis Tsoukiàs
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GBRS: A Unified Granular-Ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set
IEEE Transactions on Neural Networks and Learning Systems, 2022Pawlak rough set (PRS) and neighborhood rough set (NRS) are the two most common rough set theoretical models. Although the PRS can use equivalence classes to represent knowledge, it is unable to process continuous data. On the other hand, NRSs, which can
Shuyin Xia +7 more
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Active Incremental Feature Selection Using a Fuzzy-Rough-Set-Based Information Entropy
IEEE Transactions on Fuzzy Systems, 2020Feature selection is a popular technique of preprocessing data. In order to deal with dynamic or large data, incremental feature selection has been developed, in which the features selected from existing data are integrated with those mined from both ...
Xiao Zhang, Degang Chen, Yanyan Yang
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GBNRS: A Novel Rough Set Algorithm for Fast Adaptive Attribute Reduction in Classification
IEEE Transactions on Knowledge and Data Engineering, 2020Feature reduction is an important aspect of Big Data analytics on today’s ever-larger datasets. Rough sets are a classical method widely applied in attribute reduction.
Shuyin Xia +5 more
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ROUGH FUZZY SETS AND FUZZY ROUGH SETS*
International Journal of General Systems, 1990The notion of a rough set introduced by Pawlak has often been compared to that of a fuzzy set, sometimes with a view to prove that one is more general, or, more useful than the other. In this paper we argue that both notions aim to different purposes.
Dubois, Didier, Prade, Henri
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Communications of the ACM, 1995
Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge ...
Pawlak, Zdzisław +3 more
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Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge ...
Pawlak, Zdzisław +3 more
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Intuitionistic Fuzzy Rough Set-Based Granular Structures and Attribute Subset Selection
IEEE transactions on fuzzy systems, 2019Attribute subset selection is an important issue in data mining and information processing. However, most automatic methodologies consider only the relevance factor between samples while ignoring the diversity factor.
Anhui Tan +5 more
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2015
As quantitative generalizations of Pawlak rough sets, probabilistic rough sets consider degrees of overlap between equivalence classes and the set. An equivalence class is put into the lower approximation if the conditional probability of the set, given the equivalence class, is equal to or above one threshold; an equivalence class is put into the ...
Yao, Yiyu +2 more
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As quantitative generalizations of Pawlak rough sets, probabilistic rough sets consider degrees of overlap between equivalence classes and the set. An equivalence class is put into the lower approximation if the conditional probability of the set, given the equivalence class, is equal to or above one threshold; an equivalence class is put into the ...
Yao, Yiyu +2 more
openaire +1 more source