Compact Rule Learner on Weighted Fuzzy Approximation Spaces for Class Imbalanced and Hybrid Data

Document type: Conference Papers
Peer reviewed: Yes
Author(s): Yang Liu, Boqin Feng, Guohua Bai
Title: Compact Rule Learner on Weighted Fuzzy Approximation Spaces for Class Imbalanced and Hybrid Data
Conference name: 6th International Conference, RSCTC 2008
Year: 2008
Pagination: 262-271
ISBN: 978-3-540-88423-1
Publisher: Springer-Verlag Berlin Heidelberg
City: Akron, OH, USA
URI/DOI: 10.1007/978-3-540-88425-5_27
Organization: Blekinge Institute of Technology
Department: School of Engineering - Dept. of Interaction and System Design (Sektionen för teknik – adv. för interaktion och systemdesign)
School of Engineering S- 372 25 Ronneby
+46 455 38 50 00
Authors e-mail:
Language: English
Abstract: Rough set theory is an efficient tool for machine learning and knowledge acquisition. By introducing weightiness into a fuzzy approximation space, a new rule induction algorithm is proposed, which combines three types of uncertainty: weightiness, fuzziness and roughness. We first define the key concepts of block, minimal complex and local covering in a weighted fuzzy approximation space, then a weighted fuzzy approximation space based rule learner, and finally a weighted certainty factor for evaluating fuzzy classification rules. The time complexity of proposed rule learner is theoretically analyzed. Furthermore, in order to estimate the performance of the proposed method on class imbalanced and hybrid datasets, we compare our method with classical methods by conducting experiments on fifteen datasets. Comparative studies indicate that rule sets extracted by this method get a better performance on minority class than other approaches. It is therefore concluded that the proposed rule learner is an effective method for class imbalanced and hybrid data learning.
Subject: Computer Science\Artificial Intelligence
Computer Science\Computersystems
Computer Science\General
Keywords: Rule induction; fuzzy rough set; weighted rough set; hybrid attributes; class imbalanced data sets