A Tsetlin Machine with Multigranular Clauses
This work addresses the configuration complexity for users of Tsetlin Machines, though it is incremental as it builds directly on the existing TM framework.
The paper tackles the need for extensive hyperparameter tuning in Tsetlin Machines by introducing the Multigranular Tsetlin Machine (MTM), which eliminates a key hyperparameter and reduces the search space to two dimensions, achieving similar performance on synthetic and real-world datasets.
The recently introduced Tsetlin Machine (TM) has provided competitive pattern recognition accuracy in several benchmarks, however, requires a 3-dimensional hyperparameter search. In this paper, we introduce the Multigranular Tsetlin Machine (MTM). The MTM eliminates the specificity hyperparameter, used by the TM to control the granularity of the conjunctive clauses that it produces for recognizing patterns. Instead of using a fixed global specificity, we encode varying specificity as part of the clauses, rendering the clauses multigranular. This makes it easier to configure the TM because the dimensionality of the hyperparameter search space is reduced to only two dimensions. Indeed, it turns out that there is significantly less hyperparameter tuning involved in applying the MTM to new problems. Further, we demonstrate empirically that the MTM provides similar performance to what is achieved with a finely specificity-optimized TM, by comparing their performance on both synthetic and real-world datasets.