Variable precision rough set model
Journal of Computer and System Sciences
Knowledge representation: logical, philosophical and computational foundations
Knowledge representation: logical, philosophical and computational foundations
Data mining: concepts and techniques
Data mining: concepts and techniques
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Machine Learning by Function Decomposition
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
RSES and RSESlib - A Collection of Tools for Rough Set Computations
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Handbook of data mining and knowledge discovery
Handbook of data mining and knowledge discovery
Towards the Semantic Web: Ontology-driven Knowledge Management
Towards the Semantic Web: Ontology-driven Knowledge Management
Ontological Engineering
A view on rough set concept approximations
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Rough set approach to sunspot classification problem
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Hierarchical Rough Classifiers
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Satisfiability judgement under incomplete information
Transactions on Rough Sets XI
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This paper investigates the concept approximation problem using ontology as an domain knowledge representation model and rough set theory. In [7] [8], we have presented a rough set based multi-layered learning framework for approximation of complex concepts assuming the existence of a simple concept hierarchy. The proposed methodology utilizes the ontology structure to learn compound concepts using the rough approximations of the primitive concepts as input attributes. In this paper we consider the extended model for knowledge representation where the concept hierarchies are embedded with additional knowledge in a form of relations or constrains among sub-concepts. We present an extended multi-layered learning scheme that can incorporate the additional knowledge and propose some classes of such relations that assure an improvement of the learning algorithm as well as a convenience of the knowledge modeling process. We illustrate the proposed method and present some results of experiment with data from sunspot recognition problem.