On Multigranular Approximate Rough Equivalence of Sets and Approximate Reasoning

Tripathy, B. K.; Saraf, Prateek; Parida, Sudam Ch.

doi:10.1007/978-81-322-2208-8_55

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…The region between the lower and upper sets is the boundary region -a region in which a point may or may not belong to the set. A rough set is a uni-granular construct, i.e., the boundary of one knowledge granule is the issue for the definition of a rough model [27]. However, many studies have extended the original ideas toward multi-granular constructs.…”

Section: B Double-boundary Granulationmentioning

confidence: 99%

Unsupervised Fuzzy eIX: Evolving Internal-eXternal Fuzzy Clustering

Aguiar

Leite

2020

2020 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)

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Time-varying classifiers, namely, evolving classifiers, play an important role in a scenario in which information is available as a never-ending online data stream. We present a new unsupervised learning method for numerical data called evolving Internal-eXternal Fuzzy clustering method (Fuzzy eIX). We develop the notion of double-boundary fuzzy granules and elaborate on its implications. Type 1 and type 2 fuzzy inference systems can be obtained from the projection of Fuzzy eIX granules. We perform the principle of the balanced information granularity within Fuzzy eIX classifiers to achieve a higher level of model understandability. Internal and external granules are updated from a numerical data stream at the same time that the global granular structure of the classifier is autonomously evolved. A synthetic nonstationary problem called Rotation of Twin Gaussians shows the behavior of the classifier. The Fuzzy eIX classifier could keep up with its accuracy in a scenario in which offline-trained classifiers would clearly have their accuracy drastically dropped.

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Section: B Double-boundary Granulationmentioning

confidence: 99%

Unsupervised Fuzzy eIX: Evolving Internal-eXternal Fuzzy Clustering

Aguiar

Leite

2020

2020 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)

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“…Basing upon this observation, the notions of rough set based equalities were introduced by Pawlak in [42]. These concepts have been extended to the context of multigranular rough sets as can be found in [11,15,27]. A study of topological properties of these models can be found in [5,14].…”

Section: Related Workmentioning

confidence: 99%

Properties of Multigranular Rough Sets on Fuzzy Approximation Spaces and their Application to Rainfall Prediction

Tripathy¹,

Bhambhani²

2018

IJISA

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Basic rough set model introduced by Pawlak in 1982 has been extended in many directions to enhance their modeling power. One such attempt is the notion of rough sets on fuzzy approximation spaces by De et al in 1999. This basic model uses equivalence relation for its definition, which decompose the universal set into disjoint equivalence classes. These equivalence classes are called granules of knowledge. From the granular computing point of view the basic rough set model is unigranular in character. So, in order to handle more than one granular structure simultaneously, two types of multigranular rough sets, called the optimistic and pessimistic multigranular rough sets were introduced by Qian et al in 2006 and 2010 respectively. In this paper, we introduce two types of multigranular rough sets on fuzzy approximation spaces (optimistic and pessimistic), study several of their properties and illustrate how this notion can be used for prediction of rainfall. The introduced notions are explained through several examples.

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Covering-Based Pessimistic Multigranular Approximate Rough Equivalences and Approximate Reasoning

Tripathy

Parida²

2017

Smart Computing and Informatics

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On Multigranular Approximate Rough Equivalence of Sets and Approximate Reasoning

Cited by 4 publications

References 7 publications

Unsupervised Fuzzy eIX: Evolving Internal-eXternal Fuzzy Clustering

Unsupervised Fuzzy eIX: Evolving Internal-eXternal Fuzzy Clustering

Properties of Multigranular Rough Sets on Fuzzy Approximation Spaces and their Application to Rainfall Prediction

Covering-Based Pessimistic Multigranular Approximate Rough Equivalences and Approximate Reasoning

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