# A Tsetlin Machine with Multigranular Clauses

arXiv: https://arxiv.org/abs/1909.07310 · HTML: https://mlsanity.com/paper/1909.07310/

## Key facts

- Cited by 5 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 16 Sep 2019.

## Built on by (most cited first, 5 of 5)

- [Coalesced Multi-Output Tsetlin Machines with Clause Sharing](https://mlsanity.com/paper/2108.07594/) (2021)
- [Exploring Effects of Hyperdimensional Vectors for Tsetlin Machines](https://mlsanity.com/paper/2406.02648/) (2024)
- [Energy-frugal and Interpretable AI Hardware Design using Learning Automata](https://mlsanity.com/paper/2305.11928/) (2023)
- [LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines](https://mlsanity.com/paper/2604.12223/) (2026)
- [Compressed Recurrent Feedback in Tsetlin Machines: A Reproducible Boolean-FSM Study](https://mlsanity.com/paper/2609.06133/) (2026)

## Abstract (arXiv)

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.