Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

Understand

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.