Open AccessBook
Transactions on Rough Sets III
James F. Peters,Andrzej Skowron +1 more
TLDR
Rough Ethology: Towards a Biologically-Inspired Study of Collective Behavior in Intelligent Systems with Approximation Spaces and Information Granulation.Abstract:
Regular Papers.- Flow Graphs and Data Mining.- The Rough Set Exploration System.- Rough Validity, Confidence, and Coverage of Rules in Approximation Spaces.- Knowledge Extraction from Intelligent Electronic Devices.- Processing of Musical Data Employing Rough Sets and Artificial Neural Networks.- Computational Intelligence in Bioinformatics.- Rough Ethology: Towards a Biologically-Inspired Study of Collective Behavior in Intelligent Systems with Approximation Spaces.- Approximation Spaces and Information Granulation.- The Rough Set Database System: An Overview.- Rough Sets and Bayes Factor.- Formal Concept Analysis and Rough Set Theory from the Perspective of Finite Topological Approximations.- Dissertations and Monographs.- Time Complexity of Decision Trees.read more
Citations
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Book ChapterDOI
Decision-theoretic rough set models
TL;DR: It is shown that the decision-theoretic models need to consider additional issues in probabilistic rough set models.
Journal ArticleDOI
Combination entropy and combination granulation in rough set theory
Yuhua Qian,Jiye Liang +1 more
TL;DR: These results have a wide variety of applications, such as measuring knowledge content, measuring the significance of an attribute, constructing decision trees and building a heuristic function in aHeuristic reduct algorithm in rough set theory.
Journal ArticleDOI
Monotonic variable consistency rough set approaches
TL;DR: It is shown that consistency measures used so far in the definition of rough approximation lack some of monotonicity properties, and new measures within two kinds of rough set approaches are proposed: Variable Consistency Indiscernibility-based Rough Set Approaches (VC-IRSA) and Variable Consistsency Dominance-basedrough set Approaches(VC-DRSA).
Book ChapterDOI
Hierarchical Classifiers for Complex Spatio-temporal Concepts
TL;DR: The general methodology presented here is applied to approximate spatial complex concepts and spatio-temporal complex concepts defined for (un)structured complex objects, to identify the behavioral patterns ofcomplex objects, and to the automated behavior planning for such objects when the states of objects are represented by spatio/temporal concepts requiring approximation.
Book ChapterDOI
Naive Bayesian rough sets
TL;DR: A naive Bayesian decision-theoretic rough set model, or simply a naive Bayes' theorem-based rough set (NBRS) model, is proposed to integrate these two classification techniques.