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- W2172742757 abstract "The question how to manage the contradictive requirements of accuracy and compactness in classification systems remains an important question in machine learning and data mining. This paper proposes a approach that belongs to the domain of fuzzy rule-based classification and uses the method of rule granulation for error reduction and the method of rule consolidation for complexity reduction. The cooperative nature of those methods - the rules are split in a way that makes efficient rule consolidation feasible, rule consolidation is capable of further error reduction - is demonstrated in a number of experiments with nine benchmark classification problems, confirming the robustness of the proposed approach." @default.
- W2172742757 created "2016-06-24" @default.
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- W2172742757 date "2015-08-01" @default.
- W2172742757 modified "2023-09-27" @default.
- W2172742757 title "A combined method for error and complexity reduction in fuzzy rule-based classification" @default.
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- W2172742757 doi "https://doi.org/10.1109/fuzz-ieee.2015.7337806" @default.
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