Attribute reduction in interval-valued information systems based on information entropies
Jian-hua Dai, Hu Hu, Guo-jie Zheng, Qing-hua Hu, Hui-feng Han, Hong Shi
School of Computer Science and Technology, Tianjin University, Tianjin 300350, China; College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
Abstract 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.
Jian-hua Dai, Hu Hu, Guo-jie Zheng, Qing-hua Hu, Hui-feng Han, Hong Shi. Attribute reduction in interval-valued information systems based on information entropies. Front. Inform. Technol. Electron. Eng., 2016, 17(9): 919-928.
