Extracting Interpretable Features for Early Classification on Time Series
Extracting Interpretable Features for Early Classification on Time Series
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DOI：
10.1137/1.9781611972818.22
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发表时间：
2011-04
期刊：
影响因子：
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通讯作者：
Zhengzheng Xing;J. Pei;Philip S. Yu;Ke Wang
中科院分区：
文献类型：
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作者：
Zhengzheng Xing;J. Pei;Philip S. Yu;Ke Wang
时间序列数据的早期分类在医疗卫生信息学、工业生产管理、安全与安保管理等一些重要应用中具有很高的实用价值。虽然已经提出了一些分类器来实现良好的早期分类，但早期分类的可解释性在很大程度上仍然是一个悬而未决的问题。如果没有可解释的功能，应用领域专家(如医生)可能不愿采用早期分类。在本文中，我们解决了在时间序列上提取可解释特征以进行早期分类的问题。具体地说，我们提倡将局部Shaplet作为特征，它是保留在输入数据的同一空间中的时间序列的片段，因此具有高度的可解释性。我们在局部和早期提取清晰地体现目标类的局部小波纹，从而有效地进行早期分类。我们在7个基准真实数据集上的实验结果清楚地表明，我们的方法提取的局部Shapelets具有很高的解释力，可以实现有效的早期分类。
Early classification on time series data has been found highly useful in a few important applications, such as medical and health informatics, industry production management, safety and security management. While some classifiers have been proposed to achieve good earliness in classification, the interpretability of early classification remains largely an open problem. Without interpretable features, application domain experts such as medical doctors may be reluctant to adopt early classification. In this paper, we tackle the problem of extracting interpretable features on time series for early classification. Specifically, we advocate local shapelets as features, which are segments of time series remaining in the same space of the input data and thus are highly interpretable. We extract local shapelets distinctly manifesting a target class locally and early so that they are effective for early classification. Our experimental results on seven benchmark real data sets clearly show that the local shapelets extracted by our methods are highly interpretable and can achieve effective early classification.