Classification of Event Sequences Based on Temporal Relation Features
Classification of Event Sequences Based on Temporal Relation Features
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DOI：
10.1109/bigcomp57234.2023.00052
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发表时间：
2023-02
期刊：
影响因子：
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通讯作者：
K. Cheng
中科院分区：
文献类型：
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作者：
K. Cheng
时态数据不仅包括带有时间戳的原始数据，还包括具有非零持续时间的事件的时间区间。基于区间的事件序列分类一直是数据科学界的一个活跃研究课题。序列分类中的一个主要研究问题是提取有判别力的特征，以便恰当地捕捉潜在序列以实现高精度分类。先前关于序列分类的研究主要集中在使用基于时间点的特征的时间序列上。在本文中，我们提议基于时间区间之间的艾伦时态关系来定义特征。基于我们早期在时态数据建模方面的工作，我们开发了一种用于事件区间序列表示的新方案。我们描述了详细的算法并报告了实验结果。
Temporal data include not only time-stamped raw data but also time intervals for events with a non-zero duration. Classification of interval-based event sequences has been an active research topic in the data science community. One major research issue in sequence classification is to extract discriminative features that properly capture the underlying sequences for high accuracy classification. Previous research on sequence classification mainly focused on time series using time-point based features. In this paper, we propose to define features based on Allen’s temporal relations between time intervals. Based on our earlier work on temporal data modeling, we develop a novel scheme for sequence representation of event-intervals. We describe the detailed algorithms and report the experimental results.