Alexander E. Gegov

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Prism is a modular classification rule generation method based on the 'separate and conquer' approach that is alternative to the rule induction approach using decision trees also known as 'divide and conquer'. Prism often achieves a similar level of classification accuracy compared with decision trees, but tends to produce a more compact noise tolerant set(More)
Automatic generation of classification rules has been an increasingly popular technique in commercial applications such as Big Data analytics, rule based expert systems and decision making systems. However, a principal problem that arises with most methods for generation of classification rules is the overfit-ting of training data. When Big Data is dealt(More)
This paper presents an application of the novel theory of fuzzy networks for optimising models of systems characterised by uncertainty, non-linearity, modular structure and interactions. The application of the theory is demonstrated for retail price models in the context of converting a multiple rule base fuzzy system (MRBFS) into an equivalent single rule(More)