Attribute reduction in interval valued information systems based on information entropies
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.
Frontiers of Information Technology Electronic Engineering