A combined method for error and complexity reduction in fuzzy rule-based classification

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.