Design of Fuzzy Rule-based Classifiers through Granulation and Consolidation

Andri Riid 1  and Jürgo-Sören Preden 1
  • 1 Laboratory for Proactive Technologies, Tallinn University of Technology, Ehitajate tee 5, 19086, Tallinn, Estonia


This paper addresses the issue how to strike a good balance between accuracy and compactness in classification systems - still an important question in machine learning and data mining. The fuzzy rule-based classification approach proposed in current paper exploits 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 and rule consolidation itself is capable of further error reduction - is demonstrated in a number of experiments with nine benchmark classification problems. Further complexity reduction, if necessary, is provided by rule compression.

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