NEW CRITERIA FOR RULE SELECTION IN FUZZY LEARNING CLASSIFIER SYSTEMS

Designing an effective criterion for selecting the best rule is a major problem in theprocess of implementing Fuzzy Learning Classifier (FLC) systems. Conventionally confidenceand support or combined measures of these are used as criteria for fuzzy rule evaluation. In thispaper new entities namely precision and recall from the field of Information Retrieval (IR)systems is adapted as alternative criteria for fuzzy rule evaluation. Several differentcombinations of precision and recall are redesigned to produce a metric measure. These newlyintroduced criteria are utilized as a rule selection mechanism in the method of Iterative RuleLearning (IRL) of FLC. In several experiments, three standard datasets are used to compare andcontrast the novel IR based criteria with other previously developed measures. Experimentalresults illustrate the effectiveness of the proposed techniques in terms of classificationperformance and computational efficiency.