  Complex Evidence Theory (CET), an extension of the traditional D-S evidence
theory, has garnered academic interest for its capacity to articulate
uncertainty through Complex Basic Belief Assignment (CBBA) and to perform
uncertainty reasoning using complex combination rules. Nonetheless, quantifying
uncertainty within CET remains a subject of ongoing research. To enhance
decision-making, a method for Complex Pignistic Belief Transformation (CPBT)
has been introduced, which allocates CBBAs of multi-element focal elements to
subsets. CPBT's core lies in the fractal-inspired redistribution of the complex
mass function. This paper presents an experimental simulation and analysis of
CPBT's generation process along the temporal dimension, rooted in fractal
theory. Subsequently, a novel Fractal-Based Complex Belief (FCB) entropy is
proposed to gauge the uncertainty of CBBA. The properties of FCB entropy are
examined, and its efficacy is demonstrated through various numerical examples
and practical application.
