  We propose a novel way of assessing and fusing noisy dynamic data using a
Tsetlin Machine. Our approach consists in monitoring how explanations in form
of logical clauses that a TM learns changes with possible noise in dynamic
data. This way TM can recognize the noise by lowering weights of previously
learned clauses, or reflect it in the form of new clauses. We also perform a
comprehensive experimental study using notably different datasets that
demonstrated high performance of the proposed approach.
