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A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers., , and . CoRR, (2020)A generic framework for scenario exercises using models applied to water-resource management., , , , , , , , , and . Environ. Model. Softw., (2012)Shaping Up SHAP: Enhancing Stability through Layer-Wise Neighbor Selection., , , , , , and . AAAI, page 13094-13103. AAAI Press, (2024)XEM: An explainable-by-design ensemble method for multivariate time series classification., , , , and . Data Min. Knowl. Discov., 36 (3): 917-957 (2022)Combining narratives and modelling approaches to simulate fine scale and long-term urban growth scenarios for climate adaptation., , , , , , , , , and 1 other author(s). Environ. Model. Softw., (2016)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)Application of Artificial Intelligence to Aphasia Treatment., and . IEA/AIE (Vol. 2), page 907-913. ACM, (1990)Agnostic Local Explanation for Time Series Classification., , , and . ICTAI, page 432-439. IEEE, (2019)LCE: An Augmented Combination of Bagging and Boosting in Python., , , , and . CoRR, (2023)