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A.A.B/ USING DISTRIBUTION OF DATA TO ENHANCE PERFORMANCE C OF FUZZY CLASSIFICATION SYSTEMS
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Abstract
This paper considers the automatic design of fuzzy rule-based classification systems based on labeled data. The classification performance and interpretability are of major importance in these systems. In this paper، we utilize the distribution of training patterns in decision subspace of each fuzzy rule to improve its initially assigned certainty grade (i.e. rule weight). Our approach uses a punishment algorithm to reduce the decision subspace of a rule by reducing its weight، such that its performance is enhanced. Obviously، this reduction will cause the decision subspace of adjacent overlapping rules to be increased and consequently rewarding these rules. The results of computer simulations on some well-known data sets show the effectiveness of our approach

 
نویسنده: E. G. MANSOORI، M. J. ZOLGHADRI AND S. D. KATEBI
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منبع : IRANIAN JOURNAL OF FUZZY SYSTEMS - Vol. 4، No. 1 APRIL 2007
تاریخ : APRIL 2007
مطالب مرتبط
 
 A.A.B/ OPTIMIZATION OF LINEAR OBJECTIVE FUNCTION SUBJECT TO FUZZY RELATION INEQUALITIES CONSTRAINTS WITH MAX-AVERAGE COMPOSITION
 A.A.B/ DISTRIBUTED AND COLLABORATIVE FUZZY MODELING
 A.A.B/ NEW CRITERIA FOR RULE SELECTION IN FUZZY LEARNING CLASSIFIER SYSTEMS
 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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