δ-Cut Decision-Theoretic Rough Set Approach: Model and Attribute Reductions
Joint Authors
Yu, Hualong
Yang, Jingyu
Ju, Hengrong
Dou, Huili
Qi, Yong
Yu, Dongjun
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-22
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Decision-theoretic rough set is a quite useful rough set by introducing the decision cost into probabilistic approximations of the target.
However, Yao’s decision-theoretic rough set is based on the classical indiscernibility relation; such a relation may be too strict in many applications.
To solve this problem, a δ-cut decision-theoretic rough set is proposed, which is based on the δ-cut quantitative indiscernibility relation.
Furthermore, with respect to criterions of decision-monotonicity and cost decreasing, two different algorithms are designed to compute reducts, respectively.
The comparisons between these two algorithms show us the following: (1) with respect to the original data set, the reducts based on decision-monotonicity criterion can generate more rules supported by the lower approximation region and less rules supported by the boundary region, and it follows that the uncertainty which comes from boundary region can be decreased; (2) with respect to the reducts based on decision-monotonicity criterion, the reducts based on cost minimum criterion can obtain the lowest decision costs and the largest approximation qualities.
This study suggests potential application areas and new research trends concerning rough set theory.
American Psychological Association (APA)
Ju, Hengrong& Dou, Huili& Qi, Yong& Yu, Hualong& Yu, Dongjun& Yang, Jingyu. 2014. δ-Cut Decision-Theoretic Rough Set Approach: Model and Attribute Reductions. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1049405
Modern Language Association (MLA)
Ju, Hengrong…[et al.]. δ-Cut Decision-Theoretic Rough Set Approach: Model and Attribute Reductions. The Scientific World Journal No. 2014 (2014), pp.1-12.
https://search.emarefa.net/detail/BIM-1049405
American Medical Association (AMA)
Ju, Hengrong& Dou, Huili& Qi, Yong& Yu, Hualong& Yu, Dongjun& Yang, Jingyu. δ-Cut Decision-Theoretic Rough Set Approach: Model and Attribute Reductions. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1049405
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1049405