New Criteria For Rule Selection In Fuzzy Learning Classifier Systems

Designing an effective criterion for selecting the best rule is a major problem in the process of implementing Fuzzy Learning Classifier (FLC) systems. Conventionally confidence and support or combined measures of these are used as criteria for fuzzy rule evaluation. In this paper new entities namely precision and recall from the field of Information Retrieval (IR) systems is adapted as alternative criteria for fuzzy rule evaluation. Several different combinations of precision and recall are redesigned to produce a metric measure. These newly introduced criteria are utilized as a rule selection mechanism in the method of Iterative Rule Learning (IRL) of FLC. In several experiments, three standard datasets are used to compare and contrast the novel IR based criteria with other previously developed measures. Experimental results illustrate the effectiveness of the proposed techniques in terms of classification performance and computational efficiency.