LBRM Algorithm for Rule Extraction Based on Rough Membership

Xian Yong Zhang; Duo Qian Miao

doi:10.4028/www.scientific.net/AMR.791-793.1088

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Simulation on Torpedo Unsteady Hydrodynamic with the Movement in the Longitudinal Plane
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Algorithm for Linear Scheduling Method with Time and Distance Constraints
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Simulation Analysis on Vibration Mode of X-PLY Corrugated Paperboard in Transportation Engineering
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LBRM Algorithm for Rule Extraction Based on Rough Membership
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A Rapid Renumber Method for Implicit Iteration of Unstructured Grids
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Study on LSSVR Modeling Positioning Based on Particle Swarm Optimization
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A New Method for Initial Parameters Optimization of Guided Projectiles
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An EnhanEigen Algorithm Trust Model in P2P Network
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 791-793LBRM Algorithm for Rule Extraction Based on Rough...

LBRM Algorithm for Rule Extraction Based on Rough Membership

Abstract:

Rule extraction is a main goal for rough set theory. This paper mainly constructs a new algorithm (LBRM Algorithm) for rule extraction based on rough membership. The confidence principle is established based on rough membership. Thus, LBRM Algorithm is proposed by utilizing discretization and clearness strategies under the fuzzy environment, and is applied to both interval rules and general rules in fuzzy classification. LBRM Algorithm effectiveness is illustrated by a medical example. In particular, LBRM Algorithm integrates the confidence on both previous LBR Algorithm and fundamental rough membership, and has some improvements on rule confidence.