Last edited by Tygogal
Wednesday, August 5, 2020 | History

5 edition of Rough Fuzzy Hybridization found in the catalog.

Rough Fuzzy Hybridization

A New Trend in Decision-Making

  • 75 Want to read
  • 6 Currently reading

Published by Springer-Verlag Telos .
Written in English

    Subjects:
  • Artificial Intelligence,
  • Mathematical modelling,
  • Rough sets,
  • Computers,
  • Artificial Intelligence - General,
  • Computers - General Information,
  • Mathematical Models,
  • Computer Books: General,
  • Fuzzy sets,
  • Artificial Intelligence - Fuzzy Logic,
  • Decision making

  • Edition Notes

    ContributionsSankar K. Pal (Editor), Andrzej Skowron (Editor)
    The Physical Object
    FormatPaperback
    Number of Pages454
    ID Numbers
    Open LibraryOL9198312M
    ISBN 109814021008
    ISBN 109789814021005

    The fuzzy and rough evolutionary computing is relatively nascent. There are some examples of hybridization of fuzzy and rough sets in evolutionary computing. In some cases, the genetic algorithms are used to aid other hybridization techniques such as rough-fuzzy neural networks. Section II provides a review of the theoretical founda-. In computer science, a rough set, first described by Polish computer scientist Zdzisław I. Pawlak, is a formal approximation of a crisp set (i.e., conventional set) in terms of a pair of sets which give the lower and the upper approximation of the original set. In the standard version of rough set theory (Pawlak ), the lower- and upper-approximation sets are crisp sets, but in other.

    Scope and Organization of the Book References 2 Rough-Fuzzy Hybridization and Granular Computing Introduction Fuzzy Sets Rough Sets Emergence of Rough-Fuzzy Computing Granular Computing Computational Theory of Perception and f -Granulation Rough-Fuzzy Computing Rough fuzzy hybridization is a method of hybrid intelligent system or soft computing, where Fuzzy set theory is used for linguistic representation of patterns, leading to a fuzzy granulation of the feature space. Rough set theory is used to obtain dependency rules which model informative regions in the granulated feature space.. External links. Case generation.

    The hybridization of the technologies is demonstrated on architectures such as Fuzzy-Back-propagation Networks (NN-FL), Simplified Fuzzy ARTMAP (NN-FL), and Fuzzy Associative Memories. The book also gives an exhaustive discussion of FL-GA hybridization. Every architecture has been discussed in detail through illustrative examples and applications. Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processing Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models.


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Rough Fuzzy Hybridization Download PDF EPUB FB2

Written by leading experts from all over the world, the contributions demonstrate how rough-fuzzy hybridization can be made in various ways to provide flexible information processing capabilities for handling different real-life, ambiguous decision-making problems.

In the third part, we give an overview of the practical applications of fuzzy rough sets. The main focus will be on the machine-learning domain.

In particular, we review fuzzy-rough approaches for attribute selection, instance selection, classification, and by: 7. Abstract: This paper provides a broad overview of logical and black box approaches to fuzzy and rough hybridization.

The logical approaches include theoretical, supervised learning, feature selection, and unsupervised learning. The black box approaches consist of neural and evolutionary by: Rough-Fuzzy Pattern Recognition examines the important underlying theory as well as algorithms and applications, helping readers see the connections between theory and practice.

The first chapter provides an introduction to pattern recognition and data mining, including the key challenges of working with high-dimensional, real-life data sets. This book constitutes the thoroughly refereed conference proceedings of the 14th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrCheld in Halifax, Canada in October as one of the co-located conference of the Joint Rough Set Symposium, JRS   A model of the hybridization of rough and fuzzy sets is proposed in that allows for further refinements of rough fuzzy sets, applied to the task of unsupervised feature selection.

Moreover, an algorithm for unsupervised dimensionality reduction based on the hybridization of rough and fuzzy sets is proposed in [43]. This paper provides a broad overview Rough Fuzzy Hybridization book logical and black box approaches to fuzzy and rough hybridization.

The logical approaches include theoretical, supervised learning, feature selection, and unsupervised learning. The black box approaches consist of neural and evolutionary computing. Since both theories originated in the expert system domain, there are a number of research proposals that.

This book constitutes the refereed proceedings of the Third International Conference on Rough Sets and Knowledge Technology, RSKTheld in Chengdu, China, in May The 91 revised full papers.

The papers are grouped in topical sections on core rough set models and methods; related methods and hybridization; areas of application. Keywords approximate reasoning approximation spaces artificial intelligence clustering clustering algorithms computer vision data mining decision tables fuzzy sets granular computing problem solving rough set.

Rough Set-Based Neuro-Fuzzy System: /ch Neuro-fuzzy hybridization is the oldest and most popular methodology in soft computing (Mitra & Hayashi, ).

Neuro-fuzzy hybridization is known as Fuzzy. Rough-Fuzzy Pattern Recognition examines the important underlying theory as well as algorithms and applications, helping readers see the connections between theory and practice.

The first chapter provides an introduction to pattern recognition and data mining, including the key challenges of working with high-dimensional, real-life data sets. Part of the Lecture Notes in Computer Science book series (LNCS, volume ) Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume ) Rough-Fuzzy Hybridization.

Upper and Lower Probabilities of Fuzzy Events Induced by a Fuzzy. Get this from a library. Rough fuzzy hybridization: a new trend in decision-making. [Sankar K Pal; Andrzej Skowron;] -- "This volume provides a collection of twenty articles containing new material and describing the basic concepts and characterizing features of rough set theory and its integration with fuzzy.

This book constitutes the refereed conference proceedings of the 15th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrCheld in Tianjin, China in November as one of the co-located conference of the Joint Rough.

This book constitutes the refereed conference proceedings of the 15th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrCheld in Tianjin, China in November as one of the co-located conference of the Joint Rough Set Symposium, JRS Manufacturer: Springer.

Written by leading experts from all over the world, the contributions demonstrate how rough-fuzzy hybridization can be made in various ways to provide flexible information processing capabilities.

This paper provides a broad overview of logical and black box approaches to fuzzy and rough hybridization. The logical approaches include theoretical, supervised learning, feature selection, and.

Description. The International Journal of Rough Sets and Data Analysis (IJRSDA) is a multidisciplinary journal that publishes high-quality and significant research in all fields of rough sets, granular computing, and data mining techniques.

Rough set theory is a mathematical approach concerned with the analysis and modeling of classification and decision problems involving vague, imprecise. In this chapter, we describe an algorithm, termed as rough-fuzzy c-medoids algorithm, to select most informative bio-bases.

It comprises a judicious integration of the principles of rough sets, fuzzy sets, c-medoids algorithm, and amino acid mutation matrix. This book provides a unified framework describing how rough-fuzzy computing techniques can be formulated and used in building efficient pattern recognition models.

Based on the existing as well as new results, the book is structured according to the major phases of a pattern recognition system (e.g., classification, clustering, and feature selection) with a balanced mixture of theory.

This book constitutes the refereed conference proceedings of the 15th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrCheld in Tianjin, China in November as one of the co-located conference of the Joint Rough Set Symposium, JRS The 44 papers were carefully reviewed and selected from 97 submissions.From the Publisher: The authors consolidate a wealth of information previously scattered in disparate articles, journals, and edited volumes, explaining both the theory of neuro-fuzzy computing and the latest methodologies for performing different pattern recognition tasks in the neuro-fuzzy network - classification, feature evaluation, rule generation, knowledge extraction, and hybridization.Imprecise Knowledge and Fuzzy Modeling in Materials Domain: /ch This chapter highlights the usage of imprecise knowledge of materials systems using fuzzy inference systems.

Experts have knowledge of complex materials.