XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification
Inria（法国国家信息与自动化研究所）；Univ Rennes（雷恩大学）；CNRS（法国国家科学研究中心）；IRISA（雷恩计算机科学与应用数学研究所）；IUF（法国大学研究院）；PEGASE（PEGASE研究所）；INRAE（法国国家农业食品与环境研究所）；AGROCAMPUS OUEST（法国西部农业工程师学院）
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
