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Book ChapterDOI

Granular computing: an introduction

Witold Pedrycz
- Vol. 3, pp 1349-1354
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TLDR
The intent of the paper is to elaborate on the fundamentals of granular computing and put the entire area in a certain perspective while not moving into specific algorithmic details.
Abstract
The study is concerned with the fundamentals of granular computing. Granular computing, as the name itself stipulates, deals with representing information in the form of some aggregates (that embrace a number of individual entities) and their ensuing processing. We elaborate on the rationale behind granular computing. Next, a number of formal frameworks of information granulation are discussed including several alternatives such as fuzzy sets, interval analysis, rough sets, and probability. The notion of granularity itself is defined and quantified. A design agenda of granular computing is formulated and the key design problems are raised. A number of granular architectures are also discussed with an objective of delineating the fundamental algorithmic, and conceptual challenges. It is shown that the use of information granules of different size (granularity) lends itself to general pyramid architectures of information processing. The role of encoding and decoding mechanisms visible in this setting is also discussed in detail, along with some particular solutions. We raise an issue of interoperability of granular environments. The intent of the paper is to elaborate on the fundamentals and put the entire area in a certain perspective while not moving into specific algorithmic details.

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Citations
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Journal ArticleDOI

SVMs Modeling for Highly Imbalanced Classification

TL;DR: Of the four SVM variations considered in this paper, the novel granular SVMs-repetitive undersampling algorithm (GSVM-RU) is the best in terms of both effectiveness and efficiency.
Journal ArticleDOI

Relationship between generalized rough sets based on binary relation and covering

TL;DR: The equivalency between this type of covering-based rough sets and a type of binary relation based rough sets is established and axiomatic systems for this type-of-covering lower and upper approximation operations are presented.
Journal ArticleDOI

On Three Types of Covering-Based Rough Sets

TL;DR: The relationships among the definable sets are investigated, and certain conditions that the union of the neighborhood and the complementary neighborhood is equal to the indiscernible neighborhood are presented.
Journal ArticleDOI

Generalized rough sets based on relations

TL;DR: This paper studies arbitrary binary relation based generalized rough sets, in which a binary relation can generate a lower approximation operation and an upper approximation operation, but some of common properties of classical lower and upper approximation operations are no longer satisfied.
Journal ArticleDOI

Granular Computing: Perspectives and Challenges

TL;DR: The aim of this paper is to review foundations and schools of research and to elaborate on current developments in granular computing research.
References
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Journal ArticleDOI

Fuzzy logic = computing with words

TL;DR: The point of this note is that fuzzy logic plays a pivotal role in CW and vice-versa and, as an approximation, fuzzy logic may be equated to CW.
Journal ArticleDOI

Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic

TL;DR: M Modes of information granulation (IG) in which the granules are crisp (c-granular) play important roles in a wide variety of methods, approaches and techniques, but this does not reflect the fact that in almost all of human reasoning and concept formation thegranules are fuzzy (f- Granular).
Book

Fuzzy and neural approaches in engineering

TL;DR: Fuzzy and Neural Approaches in Engineering presents a detailed examination of the fundamentals of fuzzy systems and neural networks and then joins them synergistically - combining the feature extraction and modeling capabilities of the neural network with the representation capabilities of fuzzy Systems.
BookDOI

An introduction to fuzzy sets : analysis and design

TL;DR: Part 1 Fundamentals of fuzzy sets: basic notions and concepts of fuzzy Set Theory, types of membership functions, characteristics of a fuzzy set, basic relationships between fuzzy sets, and problem solving with fuzzy sets.