Extending the Tsetlin Machine With Integer-Weighted Clauses for Increased Interpretability

Abstract

Building models that are both interpretable and accurate is an unresolved challenge for many pattern recognition problems. In general, rule-based and linear models lack accuracy, while deep learning interpretability is based on rough approximations of the underlying inference. However, recently, the rule-based Tsetlin Machines (TMs) have obtained competitive performance in terms of accuracy, memory footprint, and inference speed on diverse benchmarks (image classification, regression, natural language understanding, and game-playing). TMs construct rules using human-interpretable conjunctive clauses in propositional logic. These, in turn, are combined linearly to solve complex pattern recognition tasks. This paper addresses the accuracy-interpretability challenge in machine learning by introducing a TM with integer weighted clauses - the Integer Weighted TM (IWTM). The intent is to increase TM interpretability by reducing the number of clauses required for competitive performance. The IWTM achieves this by weighting the clauses so that a single clause can replace multiple duplicates. Since each TM clause is formed adaptively by a Tsetlin Automata (TA) team, identifying effective weights becomes a challenging online learning problem. We solve this problem by extending each team of TA with another kind of automaton: the stochastic searching on the line (SSL) automaton. We evaluate the performance of the new scheme empirically using five datasets, along with a study of interpretability. On average, IWTM uses 6.5 times fewer literals than the vanilla TM and 120 times fewer literals than a TM with real-valued weights. Furthermore, in terms of average memory usage and F1-Score, IWTM outperforms simple Multi-Layered Artificial Neural Networks, Decision Trees, Support Vector Machines, K-Nearest Neighbor, Random Forest, Gradient Boosted Trees (XGBoost), Explainable Boosting Machines (EBMs), as well as the standard and real-value weighted TMs. IWTM finally outperforms Neural Additive Models on Fraud Detection and StructureBoost on CA-58 in terms of Area Under Curve, while performing competitively on COMPAS.