A New Information Filling Technique Based On Generalized Information Entropy

Abstract

Multi-sensor decision fusion used for discovering important facts hidden in a mass of data has become a widespread topic in recent years, and has been gradually applied in failure analysis, system evaluation and other fields of big data process. The solution to incompleteness is a key problem of decision fusion during the experiment and has been basically solved by proposed technique in this paper. Firstly, as a generalization of classical rough set, interval similarity relation is employed to classify not only single-valued data but also interval-valued data in the information systems. Then, a new kind of generalized information entropy called "H’-Information Entropy" is suggested based on interval similarity relation to measure the uncertainty and  the classification ability in the information systems. Thus, the innovated information filling technique using the properties of H’-Information Entropy can be applied to replace the missing data by some smaller estimation intervals. Finally, the feasibility and advantage of this technique are testified by two actual applications of decision fusion, whose performance is evaluated by the quantification of E-Condition Entropy