In this paper, we study the problem of classification of sequences of temporal intervals. Our main contribution is a novel framework, which we call SMILE, for extracting relevant features from interval sequences to construct classifiers.SMILE introduces the notion of utilizing random temporal abstraction features, we define as e-lets, as a means to capture information pertaining to class-discriminatory events which occur across the span of complete interval sequences. Our empirical evaluation is applied to a wide array of benchmark data sets and fourteen novel datasets for adverse drug event detection. We demonstrate how the introduction of simple sequential features, followed by progressively more complex features each improve classification performance. Importantly, this investigation demonstrates that SMILE significantly improves AUC performance over the current state-of-the-art. The investigation also reveals that the selection of underlying classification algorithm is important to achieve superior predictive performance, and how the number of features influences the performance of our framework.

在本文中，我们研究了时间间隔序列的分类问题。我们的主要贡献是一个新颖的框架，我们称之为SMILE，用于从区间序列中提取相关特征以构建分类器。SMILE引入了利用随机时态抽象特征的概念，我们将其定义为e-let，以捕获与在整个间隔序列范围内发生的类别区分事件有关的信息。我们的经验评估被广泛应用于各种基准数据集和14种新颖的药物不良事件检测数据集。我们演示了如何引入简单的顺序特征，然后逐步引入更复杂的特征，从而分别提高分类性能。重要的是，这项研究表明，SMILE与当前的最新技术相比可显着提高AUC性能。调查还显示，基础分类算法的选择对于实现出色的预测性能以及功能数量如何影响框架性能至关重要。