论文标题
XEM：多变量时间序列分类的可解释的逐日设计集合方法
XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification
论文作者
论文摘要
我们提出XEM，这是一种可解释的逐个设计集合方法，用于多元时间序列分类。 XEM依靠一种新的混合合奏方法，该方法结合了一种明确的增强障碍方法来处理机器学习模型所面临的偏见变化权衡以及一种隐性的分裂和混合方法，以在培训数据的不同部分中个性化分类器错误。我们的评估表明，XEM优于公共UEA数据集中最先进的MTS分类器。此外，XEM在面对连续数据收集（不同的MTS长度，缺少数据和噪声）面临的挑战时，提供了忠实的解释性，并表现出强劲的性能。
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).
