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A.A.B/ NEW CRITERIA FOR RULE SELECTION IN FUZZY LEARNING CLASSIFIER SYSTEMS
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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 (0 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

 
نویسنده: M. EFTEKHARI، M. J. ZOLGHADRI AND S. D. KATEBI
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منبع : IRANIAN JOURNAL OF FUZZY SYSTEMS - Vol. 3، No. 1 April 2006
تاریخ : April 2006
مطالب مرتبط
 
 A.A.B/ OPTIMIZATION OF LINEAR OBJECTIVE FUNCTION SUBJECT TO FUZZY RELATION INEQUALITIES CONSTRAINTS WITH MAX-AVERAGE COMPOSITION
 A.A.B/ USING DISTRIBUTION OF DATA TO ENHANCE PERFORMANCE C OF FUZZY CLASSIFICATION SYSTEMS
 A.A.B/ DISTRIBUTED AND COLLABORATIVE FUZZY MODELING
 A.A.B/ A PRIMER ON FUZZY OPTIMIZATION MODELS AND METHODS
 A.A.B/ MEASURING SOFTWARE PROCESSES PERFORMANCE BASED ON THE FUZZY MULTI AGENT MEASUREMENTS
 
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