A novel complex evidential distance with its application in pattern recognition

Abstract

Complex evidence theory is an effective information fusion method, which can be used to fuse complex basic belief assignments (CBBAs) and obtain a reasonable result. In complex evidence theory, it is still an open issue for conflict management. In order to address this problem, this paper focuses on putting forward a new distance to measure conflict between CBBAs. The newly defined complex evidential distance takes into account not only the singletons, but also the subsets as well as their power sets. Therefore, it has a better performance to measure the conflict between the CBBAs. In addition, the proposed distance satisfies distance properties of nonnegativity, nondegeneracy, symmetry and the triangle inequality. In particular, when the CBBAs degenerate to classical BBAs, the proposed distance can also measure conflict well. Furthermore, a number of numerical examples are given to illustrate the above mentioned properties. Based on the newly devised distance measure, a basic pattern recognition algorithm is proposed. Subsequently, it is extended to a weighted scheme of pattern recognition algorithm. Finally, those two algorithms are applied to solve medical diagnosis to demonstrate their effectiveness.