NEW CRITERIA FOR RULE SELECTION IN FUZZY LEARNING CLASSIFIER SYSTEMS

Document Type: Research Paper

Authors

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING, SHIRAZ UNIVERSITY, SHIRAZ, IRAN

Abstract

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.

Keywords


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