Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
The Paradigm of Granular Rough Computing: Foundations and Applications
COGINF '07 Proceedings of the 6th IEEE International Conference on Cognitive Informatics
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
Rough mereological classifiers obtained from weak variants of rough inclusions
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
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In data sets/decision systems, written down as pairs(U,A∪ {d}) with objects U,attributes A, and a decision d, objects aredescribed in terms of attribute---value formulas. Thisrepresentation gives rise to a calculus in terms of descriptorswhich we call a natural computing. In some recent papers,the idea of L. Polkowski of computing with granules induced fromsimilarity measures called rough inclusions have been tested. Inthis work, we pursue this topic and we study granular structuresresulting from rough inclusions with classification problem infocus. Our results show that classifiers obtained from granularstructures give better quality of classification than naturalexhaustive classifiers.