A new method for discovering rules from examples in expert systems
International Journal of Man-Machine Studies
Variable precision rough set model
Journal of Computer and System Sciences
From rough set theory to evidence theory
Advances in the Dempster-Shafer theory of evidence
Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
A comparative study of some generalized rough approximations
Fundamenta Informaticae
A Generalized Definition of Rough Approximations Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
Approximations and Rough Sets Based on Tolerances
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Indiscernibility Relation for Continuous Attributes: Application in Image Recognition
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Generating and postprocessing of biclusters from discrete value matrices
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
Mining incomplete data: a rough set approach
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
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The goal of the paper is to present the modification of classical indiscernibility relation, dedicated for rough set theory in a real-valued attributes space. Contrary to some other known generalizations, indiscernibility relation modified here, remains an equivalence relation and it is obtained by introducing a structure into collection of attributes. It defines real-valued subspaces, used in a multidimensional cluster analysis, partitioning the universe in a more natural way, as compared to one-dimensional discretization, iterated in classical model. Since the classical model is a special, extreme case of our modification, the modified version can be considered as more general. But more importantly, it allows for natural processing of real-valued attributes in a rough-set theory, broadening the scope of applications of classical, as well as variable precision rough set model, since the latter can utilize the proposed modification, equally well. In a case study, we show a real application of modified relation, a hybrid, opto-electronic recognizer of Fraunhofer diffraction patterns. Modified rough sets are used in an evolutionary optimization of the optical feature extractor implemented as a holographic ring-wedge detector. The classification is performed by a probabilistic neural network, whose error, assessed in an unbiased way is compared to earlier works.