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Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion.

, , , and . IJCNN, page 1-10. IEEE, (2023)

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Importance Reweighting for Biquality Learning., , , , and . IJCNN, page 1-8. IEEE, (2021)Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion., , , and . IJCNN, page 1-10. IEEE, (2023)Early and Revocable Time Series Classification., , , and . IJCNN, page 1-8. IEEE, (2022)From Weakly Supervised Learning to Biquality Learning, a brief introduction., , , , and . CoRR, (2020)Contrastive Representations for Label Noise Require Fine-Tuning., , , and . IAL@PKDD/ECML, volume 3079 of CEUR Workshop Proceedings, page 89-104. CEUR-WS.org, (2021)Interpretable Feature Construction for Time Series Extrinsic Regression., , , and . PAKDD (1), volume 12712 of Lecture Notes in Computer Science, page 804-816. Springer, (2021)When to Classify Events in Open Times Series?, , , and . ACML, volume 189 of Proceedings of Machine Learning Research, page 1-16. PMLR, (2022)Open challenges for Machine Learning based Early Decision-Making research., , , , , , , , and . SIGKDD Explor., 24 (2): 12-31 (2022)Apprentissage actif d'émotions dans les dialogues Homme-Machine., , and . EGC, volume RNTI-E-9 of Revue des Nouvelles Technologies de l'Information, page 427-432. Cépaduès-Éditions, (2007)FEARS: a Feature and Representation Selection approach for Time Series Classification., , , , and . ACML, volume 101 of Proceedings of Machine Learning Research, page 379-394. PMLR, (2019)