A Tsetlin Machine with Multigranular Clauses

TL;DR

The paper introduces the Multigranular Tsetlin Machine (MTM), which simplifies hyperparameter tuning by encoding clause specificity within the model, maintaining competitive accuracy with less tuning effort.

Contribution

The MTM removes the need for a fixed global specificity hyperparameter by encoding multigranular clauses, reducing hyperparameter tuning complexity.

Findings

MTM achieves similar accuracy to optimized TM on various datasets.

Hyperparameter tuning is significantly reduced with MTM.

MTM simplifies configuration without sacrificing performance.

Abstract

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.