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