Feature Based Rule Learner in Noisy Environment Using Neighbourhood Rough Set Model

Document type: Journal Articles
Article type: Original article
Peer reviewed: Yes
Author(s): Yang Liu, Luyang Jiao, Guohua Bai, Boqin Feng
Title: Feature Based Rule Learner in Noisy Environment Using Neighbourhood Rough Set Model
Journal: International Journal of Software Science and Computational Intelligence
Year: 2010
Volume: 2
Issue: 2
Pagination: 66-85
ISSN: 1942-9045
Publisher: IGI Publishing
URI/DOI: 10.4018/jssci.2010040104
Organization: Blekinge Institute of Technology
Department: School of Computing (Sektionen för datavetenskap och kommunikation)
School of Computing S-371 79 Karlskrona
+46 455 38 50 00
Language: English
Abstract: From the perspective of cognitive informatics, cognition can be viewed as the acquisition of knowledge. In real-world applications, information systems usually contain some degree of noisy data. A new model proposed to deal with the hybrid-feature selection problem combines the neighbourhood approximation and variable precision rough set models. Then rule induction algorithm can learn from selected features in order to reduce the complexity of rule sets. Through proposed integration, the knowledge acquisition process becomes insensitive to the dimensionality of data with a pre-defined tolerance degree of noise and uncertainty for misclassification. When the authors apply the method to a Chinese diabetic diagnosis problem, the hybrid-attribute reduction method selected only five attributes from totally thirty-four measurements. Rule learner produced eight rules with average two attributes in the left part of an IF-THEN rule form, which is a manageable set of rules. The demonstrated experiment shows that the present approach is effective in handling real-world problems.
Subject: Computer Science\Artificial Intelligence
Computer Science\General
Computer Science\Computersystems
Keywords: Cognitive informatics, knowledge discovery, neighbourhood approximation, rough set, attributes reduction, noisy data, LERS data mining system, rule induction, classification.