Interval Temporal Random Forests with an Application to COVID-19 Diagnosis

Authors Federico Manzella , Giovanni Pagliarini , Guido Sciavicco , Ionel Eduard Stan

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Author Details

Federico Manzella
  • Dept. of Mathematics and Computer Science, University of Ferrara, Italy
Giovanni Pagliarini
  • Dept. of Mathematics and Computer Science, University of Ferrara, Italy
  • Dept. of Mathematical, Physical, and Computer Sciences, University of Parma, Italy
Guido Sciavicco
  • Dept. of Mathematics and Computer Science, University of Ferrara, Italy
Ionel Eduard Stan
  • Dept. of Mathematics and Computer Science, University of Ferrara, Italy
  • Dept. of Mathematical, Physical, and Computer Sciences, University of Parma, Italy


We thank the INdAM GNCS 2020 project Strategic Reasoning and Automated Synthesis of Multi-Agent Systems for partial support, the PRID project Efforts in the uNderstanding of Complex interActing SystEms, the University of Udine (Italy), the University of Gothenburg (Sweden), and the Chalmers University of Technology (Sweden) for providing the computational resources, and the University of Cambridge (UK) for sharing their data. Moreover, the open access publication of this article was supported by the Alpen-Adria-Universität Klagenfurt, Austria.

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Federico Manzella, Giovanni Pagliarini, Guido Sciavicco, and Ionel Eduard Stan. Interval Temporal Random Forests with an Application to COVID-19 Diagnosis. In 28th International Symposium on Temporal Representation and Reasoning (TIME 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 206, pp. 7:1-7:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)


Symbolic learning is the logic-based approach to machine learning. The mission of symbolic learning is to provide algorithms and methodologies to extract logical information from data and express it in an interpretable way. In the context of temporal data, interval temporal logic has been recently proposed as a suitable tool for symbolic learning, specifically via the design of an interval temporal logic decision tree extraction algorithm. Building on it, we study here its natural generalization to interval temporal random forests, mimicking the corresponding schema at the propositional level. Interval temporal random forests turn out to be a very performing multivariate time series classification method, which, despite the introduction of a functional component, are still logically interpretable to some extent. We apply this method to the problem of diagnosing COVID-19 based on the time series that emerge from cough and breath recording of positive versus negative subjects. Our experiment show that our models achieve very high accuracies and sensitivities, often superior to those achieved by classical methods on the same data. Although other recent approaches to the same problem (based on different and more numerous data) show even better statistical results, our solution is the first logic-based, interpretable, and explainable one.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Machine learning algorithms
  • Interval temporal logic
  • decision trees
  • random forests
  • sound-based diagnosis


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