Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Size
This addresses the problem of interpretability and power consumption in logic-based machine learning for researchers and practitioners, though it is an incremental improvement over existing Tsetlin Machines.
The paper tackles the issue of long, less interpretable clauses in Tsetlin Machines by introducing Clause Size Constrained TMs (CSC-TMs), which enforce a soft constraint on clause size to reduce literals while maintaining or improving accuracy, achieving up to 80 times fewer literals in experiments.
Tsetlin machine (TM) is a logic-based machine learning approach with the crucial advantages of being transparent and hardware-friendly. While TMs match or surpass deep learning accuracy for an increasing number of applications, large clause pools tend to produce clauses with many literals (long clauses). As such, they become less interpretable. Further, longer clauses increase the switching activity of the clause logic in hardware, consuming more power. This paper introduces a novel variant of TM learning - Clause Size Constrained TMs (CSC-TMs) - where one can set a soft constraint on the clause size. As soon as a clause includes more literals than the constraint allows, it starts expelling literals. Accordingly, oversized clauses only appear transiently. To evaluate CSC-TM, we conduct classification, clustering, and regression experiments on tabular data, natural language text, images, and board games. Our results show that CSC-TM maintains accuracy with up to 80 times fewer literals. Indeed, the accuracy increases with shorter clauses for TREC, IMDb, and BBC Sports. After the accuracy peaks, it drops gracefully as the clause size approaches a single literal. We finally analyze CSC-TM power consumption and derive new convergence properties.