  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.
