Regularity analysis and its applications in data mining
Rough set methods and applications
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Mathematical Foundations
Rough Sets: Mathematical Foundations
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Rough mereology: a rough set paradigm for unifying rough set theory and fuzzy set theory
Fundamenta Informaticae
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
On Granular Rough Computing: Factoring Classifiers Through Granulated Decision Systems
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
A Study in Granular Computing: On Classifiers Induced from Granular Reflections of Data
Transactions on Rough Sets IX
The Paradigm of Granular Rough Computing: Foundations and Applications
COGINF '07 Proceedings of the 6th IEEE International Conference on Cognitive Informatics
Rough mereology in analysis of vagueness
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
On the idea of using granular rough mereological structures in classification of data
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
On classification of data by means of rough mereological granules of objects and rules
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Transactions on rough sets XII
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
The extraction method of DNA microarray features based on experimental A statistics
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
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In this work the subject of granular computing is pursued beyond the content of the previous paper [21]. We study here voting on a decision by granules of training objects, granules of decision rules, granules of granular reflections of training data, and granules of decision rules induced from granular reflections of training data. This approach can be perceived as a direct mapping of the training data on test ones which is induced by granulation of knowledge on the training data. Some encouraging results were already presented in [21], and here the subject is pursued systematically. Granules of knowledge are defined and computed according to a previously used scheme due to Polkowski in the framework of theory of rough inclusions. On the basis of presented results, one is justified in concluding that the presented methods offer a very good quality of classification, comparable fully with best results obtained by other rough set based methods, like templates, adaptive methods, hybrid methods etc.