Efficient Data Fusion using the Tsetlin Machine
This work addresses data fusion challenges in dynamic environments, but it appears incremental as it builds on existing Tsetlin Machine methods.
The paper tackles the problem of fusing noisy dynamic data by using a Tsetlin Machine to monitor changes in learned logical clauses, which helps recognize noise by adjusting clause weights or adding new clauses. The result is a high-performance approach demonstrated through comprehensive experiments on diverse datasets.
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.