论文标题
基于基准机器学习方法的性能 - 解释框架：应用于多元时间序列分类器
A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers
论文作者
论文摘要
我们的研究旨在提出一个新的性能解释性分析框架，以评估和基准基准机器学习方法。该框架详细介绍了一组特征，该特征会系统化现有机器学习方法的性能解释性评估。为了说明框架的使用，我们将其应用于当前最新的多元时间序列分类器。
Our research aims to propose a new performance-explainability analytical framework to assess and benchmark machine learning methods. The framework details a set of characteristics that systematize the performance-explainability assessment of existing machine learning methods. In order to illustrate the use of the framework, we apply it to benchmark the current state-of-the-art multivariate time series classifiers.
