Rule Extraction from Linguistic Rule Networks and from Fuzzy Neural Networks: Propositional versus Fuzzy Rules
نویسندگان
چکیده
This paper explores different techniques for extracting propositional rules from linguistic rule neural networks and fuzzy rules from fuzzy neural networks. The applicability and suitability of different types of rules to different problems is analyzed. Hierarchical rule structures are considered where the higher the level is the smaller the number of rules which become more vague and more approximate. The issue of quality of the rules extracted and ways to improve it is discussed. The paper takes for case study several benchmark datasets from the UCI Machine Learning Repository. It compares results produced by propositional and fuzzy rules extracted from the two types of neural networks.
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