A New Information Filling Technique Based On Generalized Information Entropy

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

A rough set approach for the discovery of classification rules in interval-valued information systems

A novel rough set approach is proposed in this paper to discover classification rules through a process of knowledge induction which selects optimal decision rules with a minimal set of features necessary and sufficient for classification of real-valued data. A rough set knowledge discovery framework is formulated for the analysis of interval-valued information systems converted from real-valued raw decision tables. The optimal feature selection method for information systems with interval-valued features obtains all classification rules hidden in a system through a knowledge induction process. Numerical examples are employed to substantiate the conceptual arguments