Stochastic and deterministic processes in Asymmetric Tsetlin Machine

TL;DR

This paper introduces new versions of the Tsetlin Machine that use randomness and asymmetry to improve decision-making and performance on complex datasets.

Contribution

The paper introduces the Asymmetric Probabilistic Tsetlin Machine and the Asymmetric Tsetlin Machine with stochastic and deterministic processes.

Findings

The Asymmetric Tsetlin Machine (AT) outperforms traditional models on complex datasets.

Both AT and APT show competitive performance compared to classical Tsetlin machines and traditional ML algorithms.

The decaying normal distribution function enhances adaptability during convergence.

Abstract

This paper introduces a new approach to enhance the decision-making capabilities of the Tsetlin Machine (TM) through the Stochastic Point Location (SPL) algorithm and the Asymmetric Steps technique. We incorporate stochasticity and asymmetry into the TM's process, along with a decaying normal distribution function that improves adaptability as it converges toward zero over time. We present two methods: the Asymmetric Probabilistic Tsetlin (APT) Machine, influenced by random events, and the Asymmetric Tsetlin (AT) Machine, which transitions from probabilistic to deterministic states. We evaluate these methods against traditional machine learning algorithms and classical Tsetlin (CT) machines across various benchmark datasets. Both AT and APT demonstrate competitive performance, with the AT model notably excelling, especially in complex datasets.

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