期刊
FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING
卷 17, 期 9, 页码 919-928出版社
ZHEJIANG UNIV PRESS
DOI: 10.1631/FITEE.1500447
关键词
Rough set theory; Interval-valued data; Attribute reduction; Entropy
类别
资金
- National Natural Science Foundation of China [61473259, 61502335, 61070074, 60703038]
- Zhejiang Provincial Natural Science Foundation [Y14F020118]
- PEIYANG Young Scholars Program of Tianjin University, China [2016XRX-0001]
Interval-valued data appear as a way to represent the uncertainty affecting the observed values. Dealing with interval-valued information systems is helpful to generalize the applications of rough set theory. Attribute reduction is a key issue in analysis of interval-valued data. Existing attribute reduction methods for single-valued data are unsuitable for interval-valued data. So far, there have been few studies on attribute reduction methods for interval-valued data. In this paper, we propose a framework for attribute reduction in interval-valued data from the viewpoint of information theory. Some information theory concepts, including entropy, conditional entropy, and joint entropy, are given in interval-valued information systems. Based on these concepts, we provide an information theory view for attribute reduction in interval-valued information systems. Consequently, attribute reduction algorithms are proposed. Experiments show that the proposed framework is effective for attribute reduction in interval-valued information systems.
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