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Fundamenta Informaticae - Special issue: rough sets
Handbook of mathematics (3rd ed.)
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Rough Sets: Theoretical Aspects of Reasoning about Data
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Feature Extraction, Construction and Selection: A Data Mining Perspective
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Feature Selection for Knowledge Discovery and Data Mining
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Rough Measures and Integrals: A Brief Introduction
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Interactive Computation: The New Paradigm
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Networks: From Biology to Theory
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Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)
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Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
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Field review: Complex systems: Network thinking
Artificial Intelligence
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Calculi of Approximation Spaces
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Near Sets. Special Theory about Nearness of Objects
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
Nearness of Objects: Extension of Approximation Space Model
Fundamenta Informaticae - Special Issue on Concurrency Specification and Programming (CS&P)
Biologically-Inspired Adaptive Learning: A Near Set Approach
FBIT '07 Proceedings of the 2007 Frontiers in the Convergence of Bioscience and Information Technologies
Handbook of Granular Computing
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Computational Statistics & Data Analysis
Feature selection: near set approach
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
Automatic planning of treatment of infants with respiratory failure through rough set modeling
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Behavioral pattern identification through rough set modelling
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Rough sets and vague concept approximation: from sample approximation to adaptive learning
Transactions on Rough Sets V
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
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RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Approximation of Loss and Risk in Selected Granular Systems
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Discovery of processes and their interactions from data and domain knowledge
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part I
Risk assessment in granular environments
Transactions on rough sets XIII
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The problem considered in this paper is the evaluation of perception as a means of optimizing various tasks. The solution to this problem hearkens back to early research on rough set theory and approximation. For example, in 1982, Ewa Orłowska observed that approximation spaces serve as a formal counterpart of perception. In this paper, the evaluation of perception is at the level of approximation spaces. The quality of an approximation space relative to a given approximated set of objects is a function of the description length of an approximation of the set of objects and the approximation quality of this set. In granular computing (GC), the focus is on discovering granules satisfying selected criteria. These criteria take inspiration from the minimal description length (MDL) principle proposed by Jorma Rissanen in 1983. In this paper, the role of approximation spaces in modeling compound granules satisfying such criteria is discussed. For example, in terms of approximation itself, this paper introduces an approach to function approximation in the context of a reinterpretation of the rough integral originally proposed by Zdzisław Pawlak in 1993. We also discuss some other examples of compound granule discovery problems that are related to compound granules representing process models and models of interaction between processes or approximation of trajectories of processes. All such granules should be discovered from data and domain knowledge. The contribution of this article is a proposed solution approach to evaluating perception that provides a basis for optimizing various tasks related to discovery of compound granules representing rough integrals, process models, their interaction, or approximation of trajectories of discovered models of processes.