A Redundancy Metric based on the Framework of Possibility Theory for Technical Systems

Detecting redundancies between information sources is essential for applications both in machine learning and information fusion. State-of-the-art redundancy metrics, such as correlation coefficients or mutual information, are based on probabilistic concepts. In technical multi-source systems information is often uncertain but also incomplete. Thus information is often provided with uncertainty distributions (probabilistic or possibilistic). In this paper a redundancy metric is proposed which is embedded in the framework of possibility theory applicable incomplete and uncertain information. The metric considers the consistency between sources, the specificity of pieces of information, and the range of observed information over the frame of discernment. The redundancy metric is designed to be cautious since incorrect identification of redundancies affects both machine learning and information fusion applications negatively. A machine learner may be deprived of information, whereas an information fusion system, relying on false assumptions, may, e.g. , incorrectly assess sources as unreliable. The proposed redundancy metric is qualitatively evaluated on information sources of three technical datasets.