Various approaches to reasoning with frequency based decision reducts: a survey
Rough set methods and applications
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Monitoring, Security, and Rescue Techniques in Multiagent Systems (Advances in Soft Computing)
Monitoring, Security, and Rescue Techniques in Multiagent Systems (Advances in Soft Computing)
A rough set-based magnetic resonance imaging partial volume detection system
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Approximate boolean reasoning: foundations and applications in data mining
Transactions on Rough Sets V
Rough Neuron based on Pattern Space Partitioning
Neurocomputing
Feedforward neural networks for compound signals
Theoretical Computer Science
Multiscale roughness measure for color image segmentation
Information Sciences: an International Journal
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Rough neural networks aim at hierarchical construction of compound concepts. Although the structure of such concepts is assumed to be more complicated than numbers in case of standard feedforward neural networks, some mechanisms can be generalized to achieve efficient propagation and learning. One of possible generalizations, called the normalizing neural networks, enables to propagate vectors instead of single signals. Neurons take form of multidimensional functions, which model cross-dependencies among importance of particular vector components. In this way, we are able to represent some types of compound concepts using relatively simple neural network structure. As an illustration, we consider the case study related to the task of magnetic resonance images' segmentation. We put a special emphasis on how the nature of objects and attributes in a given decision system influences the network's architecture. We also compare our model to other rough-neural approaches.