Complex belief interval‐based distance measure with its application in pattern recognition

Abstract

The complex evidence theory is an effective methodology for multiattribute decision-making. Since difference measure between multiattribute plays an important role for conflict management in the process of multiattribute decision-making, how to measure discrepancy between complex basic belief assignments (CBBAs) in complex evidence theory is still an open issue. In this context, a new distance measurement (complex belief distance-CBD) is proposed in this paper by taking advantages of complex belief function and complex plausibility function, called complex belief interval-based distance. In addition, we compare the proposed CBD with the related work to illustrate its superiority. Next, based on CBD, we devise a novel multiattribute decision-making algorithm for pattern recognition. Finally, we apply the method to problems of medical diagnosis to verify the effectiveness of the proposed method.

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“…DS evidence theory was proposed by Dempster and developed by Shafer as an uncertain reasoning method that can effectively integrate multi-source information. The theory is widely used in pattern classification [35], pattern recognition [36], threat assessment [37], multi-attribute decision making [38] and other fields [39][40][41][42]. In DS evidence theory,  represents the identification frame, the subsets are excluded in pairs, and 2  represents all the included identification objects in…”