2019
DOI: 10.1109/access.2019.2935416
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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, w… Show more

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Cited by 50 publications
(28 citation statements)
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“…For a machine learning tool to be useful, especially in the medical domain, it is essential for the user to be able to interpret its output. As such, it is common for studies about clinical decision support or prediction modelling to include a discussion of interpretability (29)(30)(31). However, as Lipton points out, "interpretability is not a monolithic concept" (32)it includes distinct yet intersecting ideas such as comprehension, transparency and trust.…”
Section: Discussionmentioning
confidence: 99%
“…For a machine learning tool to be useful, especially in the medical domain, it is essential for the user to be able to interpret its output. As such, it is common for studies about clinical decision support or prediction modelling to include a discussion of interpretability (29)(30)(31). However, as Lipton points out, "interpretability is not a monolithic concept" (32)it includes distinct yet intersecting ideas such as comprehension, transparency and trust.…”
Section: Discussionmentioning
confidence: 99%
“…Despite being rule-based, TMs have obtained competitive performance in terms of accuracy, memory footprint, and inference speed on diverse benchmarks, including image classification, regression, natural language understanding, and game-playing. Employing a team of TA [28], a TM learns a linear combination of conjunctive clauses in propositional logic, producing decision rules similar to the branches in a decision tree (e.g., if X satisfies condition A and not condition B then Y = 1) [23].…”
Section: Introductionmentioning
confidence: 99%
“…The TM can further be used in convolution, providing competitive performance on MNIST, Fashion-MNIST, and Kuzushiji-MNIST, in comparison with CNNs, K-Nearest Neighbour (KNN), SVMs, RF, Gradient Boosting, BinaryConnect, Logistic Circuits and ResNet [29]. The TM has also achieved promising results in text classification by using the conjunctive clauses to capture textual patterns [23]. Further, hyper-parameter search can be simplified with multi-granular clauses, eliminating the pattern specificity parameter [25].…”
Section: Introductionmentioning
confidence: 99%
“…The aim of text classification is to categorize the large-sized documents into different categories on the basis of its contents (Mohammad et al , 2018). There exist numerous methods for performing text-related tasks such as profiling users, sentiment analysis and identification of spams, which is considered as a supervised learning issue and is addressed with text classifiers (Berge et al , 2019). The text classifier contains different sub-processes in which some of them are more flexible such that it can be adapted for solving the issues of supervised learning whereas other classifiers are specially developed for addressing a specific task using expensive processes such as syntactic analysis and lemmatization (Tellez et al , 2018).…”
Section: Introductionmentioning
confidence: 99%