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Documents authored by Pagliarini, Giovanni

Document
DOI: 10.4230/LIPIcs.TIME.2022.13
Neural-Symbolic Temporal Decision Trees for Multivariate Time Series Classification

Authors: Giovanni Pagliarini, Simone Scaboro, Giuseppe Serra, Guido Sciavicco, and Ionel Eduard Stan

Published in: LIPIcs, Volume 247, 29th International Symposium on Temporal Representation and Reasoning (TIME 2022)

Abstract
Multivariate time series classification is a widely known problem, and its applications are ubiquitous. Due to their strong generalization capability, neural networks have been proven to be very powerful for the task, but their applicability is often limited by their intrinsic black-box nature. Recently, temporal decision trees have been shown to be a serious alternative to neural networks for the same task in terms of classification performances, while attaining higher levels of transparency and interpretability. In this work, we propose an initial approach to neural-symbolic temporal decision trees, that is, an hybrid method that leverages on both the ability of neural networks of capturing temporal patterns and the flexibility of temporal decision trees of taking decisions on intervals based on (possibly, externally computed) temporal features. While based on a proof-of-concept implementation, in our experiments on public datasets, neural-symbolic temporal decision trees show promising results.
Cite as

Giovanni Pagliarini, Simone Scaboro, Giuseppe Serra, Guido Sciavicco, and Ionel Eduard Stan. Neural-Symbolic Temporal Decision Trees for Multivariate Time Series Classification. In 29th International Symposium on Temporal Representation and Reasoning (TIME 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 247, pp. 13:1-13:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{pagliarini_et_al:LIPIcs.TIME.2022.13,
  author =	{Pagliarini, Giovanni and Scaboro, Simone and Serra, Giuseppe and Sciavicco, Guido and Stan, Ionel Eduard},
  title =	{{Neural-Symbolic Temporal Decision Trees for Multivariate Time Series Classification}},
  booktitle =	{29th International Symposium on Temporal Representation and Reasoning (TIME 2022)},
  pages =	{13:1--13:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-262-4},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{247},
  editor =	{Artikis, Alexander and Posenato, Roberto and Tonetta, Stefano},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2022.13},
  URN =		{urn:nbn:de:0030-drops-172607},
  doi =		{10.4230/LIPIcs.TIME.2022.13},
  annote =	{Keywords: Machine learning, neural-symbolic, temporal logic, hybrid temporal decision trees}
}
Document
DOI: 10.4230/LIPIcs.TIME.2021.7
Interval Temporal Random Forests with an Application to COVID-19 Diagnosis

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

Published in: LIPIcs, Volume 206, 28th International Symposium on Temporal Representation and Reasoning (TIME 2021)

Abstract
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.
Cite as

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)

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@InProceedings{manzella_et_al:LIPIcs.TIME.2021.7,
  author =	{Manzella, Federico and Pagliarini, Giovanni and Sciavicco, Guido and Stan, Ionel Eduard},
  title =	{{Interval Temporal Random Forests with an Application to COVID-19 Diagnosis}},
  booktitle =	{28th International Symposium on Temporal Representation and Reasoning (TIME 2021)},
  pages =	{7:1--7:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-206-8},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{206},
  editor =	{Combi, Carlo and Eder, Johann and Reynolds, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2021.7},
  URN =		{urn:nbn:de:0030-drops-147837},
  doi =		{10.4230/LIPIcs.TIME.2021.7},
  annote =	{Keywords: Interval temporal logic, decision trees, random forests, sound-based diagnosis}
}
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