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Granular Generalized Variable Precision Rough Sets and Rational Approximations

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arxiv 2205.14365 v3 pith:4POLUAIH submitted 2022-05-28 cs.AI cs.LGcs.LO

Granular Generalized Variable Precision Rough Sets and Rational Approximations

classification cs.AI cs.LGcs.LO
keywords approximationsgranularroughsetsgeneralizationsintroducedrationalconstructed
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Rational approximations are introduced and studied in granular graded rough sets and generalizations thereof by the first author in recent research papers. The concept of rationality is determined by related ontologies and coherence between granularity, mereology and approximations in the context. In addition, a framework for rational approximations is introduced by her in the mentioned paper(s). Granular approximations constructed as per the procedures of variable precision rough sets (VPRS) are likely to be more rational than those constructed from a classical perspective under certain conditions. This may continue to hold for some generalizations of the former. However, a formal characterization of such conditions is not available in the previously published literature. In this research, theoretical aspects of the problem are critically examined, uniform generalizations of granular VPRS are introduced, new connections with granular graded rough sets are proved, appropriate concepts of substantial parthood are introduced, their extent of compatibility with the framework is accessed, and the framework is extended. Basic assumptions are explained in detail, and additional examples are constructed for readability. Furthermore, meta applications to cluster validation, image segmentation and dynamic sorting are invented. Extensions to direct generalizations of VPRS such as probabilistic rough sets are a natural consequence of the work.

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Cited by 1 Pith paper

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  1. Handbook of Rough Set Extensions and Uncertainty Models

    cs.AI 2026-04 unverdicted novelty 1.0

    A systematic survey mapping rough set paradigms and extensions by granulation type and uncertainty frameworks such as fuzzy and neutrosophic settings.