A New Information Filling Technique Based On Generalized Information Entropy
Keywords:Multi-Sensor Decision Fusion, Interval-Valued Information System, Generalized Information Entropy, Information Classification, Information Filling
Multi-sensor decision fusion used for discovering important facts hidden inÂ a mass of data has become a widespread topic in recent years, and has been graduallyÂ applied in failure analysis, system evaluation and other fields of big data process. TheÂ solution to incompleteness is a key problem of decision fusion during the experimentÂ and has been basically solved by proposed technique in this paper. Firstly, as aÂ generalization of classical rough set, interval similarity relation is employed to classifyÂ not only single-valued data but also interval-valued data in the information systems.Â Then, a new kind of generalized information entropy called "H’-Information Entropy"Â is suggested based on interval similarity relation to measure the uncertainty and Â theÂ classification ability in the information systems. Thus, the innovated informationÂ filling technique using the properties of H’-Information Entropy can be applied toÂ replace the missing data by some smaller estimation intervals. Finally, the feasibilityÂ and advantage of this technique are testified by two actual applications of decisionÂ fusion, whose performance is evaluated by the quantification of E-Condition Entropy.
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