Information Granularity in Fuzzy Binary GrC Model

Qian, Yuhua; Liang, Jiye; Wu, Wei-Zhi; Dang, Chuangyin

doi:10.1109/tfuzz.2010.2095461

Cited by 176 publications

(69 citation statements)

References 51 publications

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“…From the viewpoint of granular computing 47 , each equivalence class in U/IND(A) is an information granule. In other words, by a given indiscernibility relation, objects are granulated into a set of information granules, called a granular structure 29 . It should be noticed that partition is only a special granular structure, granular structure may also be a set of information granules induced by a general binary relation.…”

Section: Multigranulation Rough Setsmentioning

confidence: 99%

“…Lower, upper approximations and boundary region in rough set model are then the unions of some blocks (equivalence classes) in partition with different conditions, respectively. Obviously, Pawlak's rough set is constructed on the basis of one and only one set of the information granules (set of equivalence classes in a partition), we call such set a granular structure 29 . From this point of view, Pawlak's model is referred to as a singlegranulation rough set approach in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Multigranulation rough set: A multiset based strategy

Yang¹,

Xu²,

Dou³

et al. 2017

IJCIS

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A simple multigranulation rough set approach is to approximate the target through a family of binary relations. Optimistic and pessimistic multigranulation rough sets are two typical examples of such approach. However, these two multigranulation rough sets do not take frequencies of occurrences of containments or intersections into account. To solve such problem, by the motivation of the multiset, the model of the multiple multigranulation rough set is proposed, in which both lower and upper approximations are multisets. Such two multisets are useful when counting frequencies of occurrences such that objects belong to lower or upper approximations with a family of binary relations. Furthermore, not only the concept of approximate distribution reduct is introduced into multiple multigranulation rough set, but also a heuristic algorithm is presented for computing reduct. Finally, multiple multigranulation rough set approach is tested on eight UCI (University of California-Irvine) data sets. Experimental results show: 1. the approximate quality based on multiple multigranulation rough set is between approximate qualities based on optimistic and pessimistic multigranulation rough sets; 2. by comparing with optimistic and pessimistic multigranulation rough sets, multiple multigranulation rough set needs more attributes to form a reduct.

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Section: Multigranulation Rough Setsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multigranulation rough set: A multiset based strategy

Yang¹,

Xu²,

Dou³

et al. 2017

IJCIS

View full text Add to dashboard Cite

show abstract

“…With this regard, granularity of information (Bargiela and Pedrycz 2002, 2003, 2005Apolloni etal. 2008;Srivastava et al 1999;Slȩzak 2009;Pedrycz and Song 2011;Qian et al 2011) plays a pivotal role and becomes of paramount importance, both from the conceptual as well as algorithmic perspective to the realization of granular models of time series. Subsequently, processing realized at the level of information granules gives rise to the discipline of granular computing (Bargiela and Pedrycz 2003).…”

Section: Introductionmentioning

confidence: 99%

Human-centric analysis and interpretation of time series: a perspective of granular computing

Pedrycz

Liu

et al. 2014

Soft Comput

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In spite of the truly remarkable diversity of models of time series, there is still an evident need to develop constructs whose accuracy and interpretability are carefully identified and reconciled subsequently leading to highly interpretable (human-centric) constructs. While a great deal of research has been devoted to the design of nonlinear numeric models of time series (with an evident objective to achieve high accuracy of prediction), an issue of interpretability (transparency) of models of time series becomes an evident and ongoing challenge. The user-friendliness of models of time series comes with an ability of humans to perceive and process abstract constructs rather than dealing with plain numeric entities. In perception of time series, information granules (which are regarded as realizations of interpretable entities) play a pivotal role. This gives rise to a concept of granular models of time series or granular time series, in brief. This study revisits generic concepts of information granules and elaborates on a fundamental way of forming information granules (both sets-intervals as well as fuzzy sets) through applying a principle of justifiable granularity encountered in granular computing. Information granules are discussed with regard to the granulation of time series in a certain predefined representation space (viz. a feature space) and granulation carried out in time. The granular representation and description of time series is then presented. We elaborate on the fundamental hierarchically organized layers of processing supporting the development and interpretation of granular time series, namely (a) formation of granular descriptors used in their visualization, (b) construction of linguistic descriptors used afterwards in the generation of (c) linguistic description of time series. The layer of the linguistic prediction models of time series exploiting the linguistic descriptors is outlined as well. A number of examples are offered throughout the entire paper with intent to illustrate the main functionalities of the essential layers of the granular models of time series.

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“…effective tool with vast potential for knowledge acquisition, GrC has been widely investigated by researchers in the field of artificial intelligence [5,15,17,18,20,21,24]. In addition, the fuzzy equivalence relation [30] of fuzzy set theory is introduced into FCA, and we propose a model of FCA based on the fuzzy equivalence for different granulations.…”

Section: Introductionmentioning

confidence: 99%