A new method for discovering rules from examples in expert systems
International Journal of Man-Machine Studies
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
An analytical framework for local feedforward networks
IEEE Transactions on Neural Networks
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The paper compares two types of classifiers in a hybrid opto-electronic pattern recognition system. The first type is rough set based classifier operating is highly discretized feature space. This is the consequence of the granular nature of knowledge representation in the theory of rough sets. The second type is artificial neural network, which processes information taken from continuous feature space. The paper deals with the issues which arise when these two types of feature space coexist in one pattern recognition problem. In particular these issues are illustrated in the example of system used for recognition of speckle images of intermodal interference. In both cases the feature extraction is performed with the use of holographic ring wedge detector, generating the continuous feature space. This is the feature space natural for application of the artificial neural network in a classification subsystem. However, the optimization of feature extractor proposed in earlier papers, uses rough set theory, requiring the discretization of conditional attributes generating the feature space. Therefore such optimization is more suitable for rough set classifiers. Advantages and drawbacks of both solutions are presented in the paper. As the conclusion the new method of optimization of holographic ring wedge detector is postulated.