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• W4210974218 abstract "We present XEM, an eXplainable-by-design Ensemble method for Multivariate time series classification. XEM relies on a new hybrid ensemble method that combines an explicit boosting-bagging approach to handle the bias-variance trade-off faced by machine learning models and an implicit divide-and-conquer approach to individualize classifier errors on different parts of the training data. Our evaluation shows that XEM outperforms the state-of-the-art MTS classifiers on the public UEA datasets. Furthermore, XEM provides faithful explainability-by-design and manifests robust performance when faced with challenges arising from continuous data collection (different MTS length, missing data and noise)." @default.
• W4210974218 created "2022-02-13" @default.
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• W4210974218 date "2022-02-11" @default.
• W4210974218 modified "2023-10-17" @default.
• W4210974218 title "XEM: An explainable-by-design ensemble method for multivariate time series classification" @default.
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• W4210974218 doi "https://doi.org/10.1007/s10618-022-00823-6" @default.
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