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Conference Papers Year : 2021

A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers

Abstract

Our research aims to propose a new performance-explainability analytical framework to assess and benchmark machine learning methods. The framework details a set of characteristics that systematize the performance-explainability assessment of existing machine learning methods. In order to illustrate the use of the framework, we apply it to benchmark the current state-of-the-art multivariate time series classifiers.
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Dates and versions

hal-03094885 , version 1 (04-01-2021)
hal-03094885 , version 2 (20-12-2021)

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Cite

Kevin Fauvel, Véronique Masson, Elisa Fromont. A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers. IJCAI-PRICAI 2020 - Workshop on Explainable Artificial Intelligence (XAI), Jan 2021, Yokohama, Japan. pp.1-8. ⟨hal-03094885v2⟩
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