Rudiments of rough sets

Pawlak, Zdziasław; Skowron, Andrzej

doi:10.1016/j.ins.2006.06.003

Cited by 1,877 publications

(851 citation statements)

References 237 publications

Supporting

Mentioning

845

Contrasting

Unclassified

Order By: Relevance

“…In general, finding the optimal solution, that is which minimizes the number of observed components while ensuring baseness or completeness is hard. This problem is actually similar to the identification of the minimal number of attributes required to distinguish a set of objects in the rough set theory [40] which is known to be NP-hard. Each of the proposed heuristics guarantees that the reduced Restriction ρ built using the returned sets of observed components meets the corresponding detection level.…”

Section: Heuristics To Minimize Observed Componentsmentioning

confidence: 99%

See 1 more Smart Citation

Optimized distributed implementation of multiparty interactions with Restriction

Bensalem

Bozga

Quilbeuf

et al. 2015

Science of Computer Programming

View full text Add to dashboard Cite

Using high level coordination primitives allows enhanced expressiveness of componentbased frameworks to cope with the inherent complexity of present-day systems designs. Nonetheless, their distributed implementation raises multiple issues, regarding both the correctness and the runtime performance of the final implementation. We propose a novel approach for distributed implementation of multiparty interactions subject to scheduling constraints expressed by priorities. We rely on a new composition operator named Restriction, whose semantics dynamically restricts the set of interactions allowed for execution, depending on the current state. We show that this operator provides a natural encoding for priorities. We provide a knowledge-based optimization that modifies the Restriction operator to avoid superfluous communication in the final implementation. We complete our framework through an enhanced conflict resolution protocol that natively implements Restriction. A prototype implementation allows us to compare performances of different optimizations.

show abstract

Section: Heuristics To Minimize Observed Componentsmentioning

confidence: 99%

“…The formalization of different kinds of knowledge and related logic, called epistemic logic, have been intensively studied [22,29,30]. In rough sets theory [40], objects are defined through a set of attributes. Intuitively, an attribute can be the shape, the color, the weight.…”

Section: Related Workmentioning

confidence: 99%

Optimized distributed implementation of multiparty interactions with Restriction

Bensalem

Bozga

Quilbeuf

et al. 2015

Science of Computer Programming

View full text Add to dashboard Cite

show abstract

“…Near sets [67,[78][79][80][81]83] offer a generalization of traditional rough set theory [84][85][86][87][88] and a new approach to classifying perceptual objects by means of features [89][90][91][92][93][94]. The near set approach can be used to classify images that are qualitatively but not necessary quantitatively close to each other.…”

Section: Near Sets: Generalization Of the Rough Set In Multimedia Promentioning

confidence: 99%

Computational Intelligence in Multimedia Processing: Foundation and Trends

Hassanien

Abraham²,

Kacprzyk³

et al. 2008

Studies in Computational Intelligence

View full text Add to dashboard Cite

Summary. This chapter presents a broad overview of Computational Intelligence (CI) techniques including Neural Network (NN), Particle Swarm Optimization (PSO), Evolutionary Algorithm (GA), Fuzzy Set (FS), and Rough Sets (RS). In addition, a very brief introduction to near sets and near images which offer a generalization of traditional rough set theory and a new approach to classifying perceptual objects by means of features in solving multimedia problems is presented. A review of the current literature on CI based approaches to various problems in multimedia computing such as speech, audio and image processing, video watermarking, content-based multimedia indexing and retrieval are presented. We discuss some representative methods to provide inspiring examples to illustrate how CI could be applied to resolve multimedia computing problems and how multimedia could be analyzed, processed, and characterized by computational intelligence. Challenges to be addressed and future directions of research are also presented.

show abstract

“…One of the main areas of applications for the approach is the rough set theory [10,11] in which decision trees and rules are widely used. This approach was extended also to some combinatorial optimization problems [1,[3][4][5].…”

Section: Introductionmentioning

confidence: 99%