Title: Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications,
Abstract: Medical applications challenge today's text categorization techniques by
demanding both high accuracy and ease-of-interpretation. Although deep learning
has provided a leap ahead in accuracy, this leap comes at the sacrifice of
interpretability. To address this accuracy-interpretability challenge, we here
introduce, for the first time, a text categorization approach that leverages
the recently introduced Tsetlin Machine. In all brevity, we represent the terms
of a text as propositional variables. From these, we capture categories using
simple propositional formulae, such as: if "rash" and "reaction" and
"penicillin" then Allergy. The Tsetlin Machine learns these formulae from a
labelled text, utilizing conjunctive clauses to represent the particular facets
of each category. Indeed, even the absence of terms (negated features) can be
used for categorization purposes. Our empirical comparison with Naïve Bayes,
decision trees, linear support vector machines (SVMs), random forest, long
short-term memory (LSTM) neural networks, and other techniques, is quite
conclusive. The Tsetlin Machine either performs on par with or outperforms all
of the evaluated methods on both the 20 Newsgroups and IMDb datasets, as well
as on a non-public clinical dataset. On average, the Tsetlin Machine delivers
the best recall and precision scores across the datasets. Finally, our GPU
implementation of the Tsetlin Machine executes 5 to 15 times faster than the
CPU implementation, depending on the dataset. We thus believe that our novel
approach can have a significant impact on a wide range of text analysis
applications, forming a promising starting point for deeper natural language
understanding with the Tsetlin Machine.