In the process of early classification, earliness and accuracy are two key indicators to evaluate the performance of classification, and early classification usually weaken its accuracy to some degree. Therefore, how to find a tradeoff between two conflict objectives is a challenging work. So far, there are just a few work touched the quality of early classification on univariate time series, and the confidence estimation for early classification on multivariate time series (MTS) is still an open issue. In this paper, we focus on interpretably classifying MTS examples as early as possible while guaranteeing the quality of the classification results. First, a fast method is proposed to mine interpretable and local rules from the MTS training data. Second, a valid measure is advanced to estimate the confidence of early classification on MTS examples. Finally, a strategy is designed to execute confident early classification to assume the classification confidence meets customers’ requirement. Experiment results on seven datasets show that the effectiveness and efficiency of our proposed algorithm for confident early classification on multivariate time series.

中文翻译：

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具有多个可解释规则的基于时间的多元时间序列的早期分类

在早期分类的过程中，早期性和准确性是评估分类性能的两个关键指标，早期分类通常会在一定程度上削弱其准确性。因此，如何在两个冲突目标之间进行权衡是一项具有挑战性的工作。到目前为止，只有少数工作触及单变量时间序列的早期分类的质量，而对多元时间序列（MTS）的早期分类的置信度估计仍然是一个未解决的问题。在本文中，我们致力于尽早对MTS示例进行可解释的分类，同时保证分类结果的质量。首先，提出了一种从MTS训练数据中挖掘可解释和本地规则的快速方法。其次，提出了一种有效的措施来估计MTS示例早期分类的可信度。最终，设计了一种策略来执行自信的早期分类，以假定分类的可信度满足客户的要求。在七个数据集上的实验结果表明，我们提出的算法对多变量时间序列的置信早期分类的有效性和效率。