A new approach for generation of generalized basic probability assignment in the evidence theory

TL;DR

This paper introduces a novel method for generating generalized basic probability assignments in evidence theory, enhancing multi-source information fusion under uncertain, incomplete, and complex conditions.

Contribution

It proposes a new approach using triangular fuzzy numbers for BPA generation under open world assumptions, improving flexibility and reducing information loss.

Findings

Method effectively handles incomplete information.

Experiments show improved accuracy on UCI datasets.

Approach is adaptable to complex environments.

Abstract

The process of information fusion needs to deal with a large number of uncertain information with multi-source, heterogeneity, inaccuracy, unreliability, and incompleteness. In practical engineering applications, Dempster-Shafer evidence theory is widely used in multi-source information fusion owing to its effectiveness in data fusion. Information sources have an important impact on multi-source information fusion in an environment of complex, unstable, uncertain, and incomplete characteristics. To address multi-source information fusion problem, this paper considers the situation of uncertain information modeling from the closed world to the open world assumption and studies the generation of basic probability assignment (BPA) with incomplete information. In this paper, a new method is proposed to generate generalized basic probability assignment (GBPA) based on the triangular fuzzy number model under the open world assumption. The proposed method can not only be used in different complex environments simply and flexibly, but also have less information loss in information processing. Finally, a series of comprehensive experiments basing on the UCI data sets are used to verify the rationality and superiority of the proposed method.