The regression Tsetlin machine: a novel approach to interpretable nonlinear regression

Abstract

Relying simply on bitwise operators, the recently introduced <i>Tsetlin machine</i> (TM) has provided competitive pattern classification accuracy in several benchmarks, including text understanding. In this paper, we introduce the <i>regression Tsetlin machine</i> (RTM), a new class of TMs designed for continuous input and output, targeting nonlinear regression problems. In all brevity, we convert continuous input into a binary representation based on thresholding, and transform the propositional formula formed by the TM into an aggregated continuous output. Our empirical comparison of the RTM with state-of-the-art regression techniques reveals either superior or on par performance on five datasets. This article is part of the theme issue 'Harmonizing energy-autonomous computing and intelligence'.