Rough Computing

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Overview

Rough set theory is a new soft computing tool which deals with vagueness and uncertainty. It has attracted the attention of researchers and practitioners worldwide, and has been successfully applied to many fields such as knowledge discovery, decision support, pattern recognition, and machine learning.

Rough Computing: Theories, Technologies and Applications offers the most comprehensive coverage of key rough computing research, surveying a full range of topics from granular computing to pansystems theory. With its unique coverage of the defining issues of the field, this commanding research collection provides libraries with a single, authoritative reference to this highly advanced technological topic.

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Product Details

  • ISBN-13: 9781599045528
  • Publisher: IGI Global
  • Publication date: 8/31/2007
  • Pages: 314
  • Product dimensions: 8.50 (w) x 11.00 (h) x 0.75 (d)

Table of Contents


Preface     x
Acknowledgment     xiii
Foundations of Rough Sets
Foundations of Rough Sets from Vagueness Perspective   Piotr Wasilewski   Dominik Slezak     1
Rough Sets and Boolean Reasoning   Hung Sow Nguyen     38
Rough Set-Based Feature Selection: A Review   Richard Jensen   Qiang Shen     70
Rough Set Analysis and Formal Concept Analysis   Yiyu Yao   Yaohua Chen     108
Current Trends and Models
Rough Sets: A Versatile Theory for Approaches to Uncertainty Management in Databases   Theresa Beaubouef   Frederick E. Petry     129
Current Trends in Rough Set Flow Graphs   Cory J. Butz   Wen Yan     152
Probabilistic Indices of Quality of Approximation   Annibal Parracho Sant'Anna     162
Extended Action Rule Discovery Based on Single Classification Rules and Reducts   Zbigniew W. Ras   Elzbieta M. Wyrzykowska     175
Rough Sets and Hybrid Systems
Monocular Vision System that Learns with Approximation Spaces   James F. Peters   Maciej Borkowski   Christopher Henry   Dan Lockery     186
Hybridization of Rough Sets and Multi-Objective Evolutionary Algorithms for Classificatory Signal Decomposition   Tomasz G. Smolinski, Astrid A. Prinz   Jacek M. Zurada     204
Two Rough Set Approaches to Mining Hop Extraction Data   Jerzy W. Grzymala-Busse   Zdzislaw S. Hippe   Teresa Mroczek   Edward Roj   Boleslaw Skowronski     228
Rough Sets for Discovering Concurrent System Models from Data Tables   Krzysztof Pancerz   Zbigniew Suraj     239
Compilation of References     269
About the Contributors     290
Index     297
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