The Weighted Tsetlin Machine: Compressed Representations with Weighted Clauses

TL;DR

The paper introduces the Weighted Tsetlin Machine (WTM), a more efficient and compact version of the TM that uses clause weighting and a novel sampling scheme to improve accuracy, reduce resource usage, and speed up training.

Contribution

The WTM incorporates clause weighting and a new sampling method, enabling better performance and efficiency compared to the original Tsetlin Machine.

Findings

WTM matches TM accuracy with fewer clauses on MNIST, IMDb, and Connect-4.

WTM outperforms TM with the same number of clauses, achieving higher test accuracies.

Sampling scheme reduces sample generation time by a factor of 7.

Abstract

The Tsetlin Machine (TM) is an interpretable mechanism for pattern recognition that constructs conjunctive clauses from data. The clauses capture frequent patterns with high discriminating power, providing increasing expression power with each additional clause. However, the resulting accuracy gain comes at the cost of linear growth in computation time and memory usage. In this paper, we present the Weighted Tsetlin Machine (WTM), which reduces computation time and memory usage by weighting the clauses. Real-valued weighting allows one clause to replace multiple, and supports fine-tuning the impact of each clause. Our novel scheme simultaneously learns both the composition of the clauses and their weights. Furthermore, we increase training efficiency by replacing $k$ Bernoulli trials of success probability $p$ with a uniform sample of average size $p k$, the size drawn from a binomial distribution. In our empirical evaluation, the WTM achieved the same accuracy as the TM on MNIST, IMDb, and Connect-4, requiring only $1/4$, $1/3$, and $1/50$ of the clauses, respectively. With the same number of clauses, the WTM outperformed the TM, obtaining peak test accuracies of respectively $98.63\%$, $90.37\%$, and $87.91\%$. Finally, our novel sampling scheme reduced sample generation time by a factor of $7$.