Publications
Lipeng Pan, Yong Deng, Danilo Pelusi, A similarity measure of complex-valued evidence theory for multi-source information fusion, Information Sciences, 2023, https://doi.org/10.1016/j.ins.2023.119416.
Abstract: The study of complex-valued evidence theory has become an interesting topic, particularly in the context of information fusion techniques. These studies mainly focus on its geometric interpretation and application. Moreover, little work has been conducted with regard to conflict measure. Therefore, this paper defines a complex similarity measure which quantifies the degree of conflict among complex-valued mass functions by measuring the degree of consistency among complex-valued mass functions. It also satisfies some basic properties, such as non-negativity, symmetry and etc. Complex similarity measure is an extension of traditional similarity measure in complex space, which will collapse to a similarity measure of real space when complex-valued mass functions collapse to mass functions of real space. Furthermore, some numerical examples illustrate the rationality and effectiveness of complex similarity measure. By employing complex similarity measure, this paper proposes a method to combine information and reduce the time cost associated with complex-valued Dempster's rule of combination. Experimental results on datasets indicate that for some data sets (Take iris for example), the proposed method can not only reduce the time cost (75%), but also improve the accuracy of fusion results (96%).
Linlu Gao, Fuyuan Xiao, Danilo Pelusi, A complex belief χ2 divergence in complex evidence theory and its application for pattern classification, Engineering Applications of Artificial Intelligence, Vol. 126, Part A, 2023, https://doi.org/10.1016/j.engappai.2023.106752.
Abstract: Complex evidence theory (CET) is crucial in modeling uncertain information in the complex domain. With the development of the research on CET, how to measure the conflict between complex basic belief assignments (CBBAs) accurately remains an open issue. Thus, we propose the complex belief χ2 divergence (called CBχ2 divergence) tending to solve conflict management issues. The proposed complex belief χ2 divergence which considers the quantum interference effects is a generalized model of traditional belief χ2 divergence. Furthermore, the proposed complex belief χ2 divergence satisfies the properties of symmetry, non-degeneracy and continuity. Besides, an algorithm for decision-making based on the complex belief χ2 divergence is proposed. This method is applied in several datasets from UCI machine learning repository including the iris dataset, the seeds dataset and the heart disease dataset for pattern recognition. The result shows the proposed method has better classification accuracy compared with the related methods.