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

, , and . CoRR, (2020)

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Enhancing Performance and Explainability of Multivariate Time Series Machine Learning Methods: Applications for Social Impact in Dairy Resource Monitoring and Earthquake Early Warning. (Amélioration de la Performance et de l'Explicabilité des Méthodes d'Apprentissage Automatique de Séries Temporelles Multivariées).. University of Rennes, Inria, France, (2020)A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers., , and . CoRR, (2020)MDSC: modelling distributed stream processing across the edge-to-cloud continuum., , , , , and . UCC Companion, page 25:1-25:6. ACM, (2021)A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification., , , , and . CoRR, (2022)LCE: An Augmented Combination of Bagging and Boosting in Python., , , , and . CoRR, (2023)A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification., , and . KDD, page 4013-4023. ACM, (2023)XEM: An explainable-by-design ensemble method for multivariate time series classification., , , , and . Data Min. Knowl. Discov., 36 (3): 917-957 (2022)AppClassNet: a commercial-grade dataset for application identification research., , , , and . Comput. Commun. Rev., 52 (3): 19-27 (2022)Local Cascade Ensemble for Multivariate Data Classification., , , , and . CoRR, (2020)A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning., , , , , , , , , and 1 other author(s). AAAI, page 403-411. AAAI Press, (2020)