Time series classification based on multi-feature dictionary representation and ensemble learning
Time series classification based on multi-feature dictionary representation and ensemble learning
复制标题
基于多特征字典表示和集成学习的时间序列分类
DOI：
10.1016/j.eswa.2020.114162
复制
发表时间：
2021-05-01
影响因子：
8.5
通讯作者：
Yan, Wenhe
中科院分区：
文献类型：
--
作者：
Bai, Bing;Li, Guiling;Yan, Wenhe
时间序列分类是时间序列数据挖掘的一项重要任务，人们提出了许多高层次的时间序列表示方法来解决这个问题。符号聚集近似(SAX)是一种经典的高级符号表示方法，能有效地对时间序列进行降维。然而，基于SAX的时间序列分类方法并不能取得令人满意的结果，因为SAX只提取子序列的平均特征来进行符号化。本文提出了一种基于SAX的集成方法TBOPE，该方法基于多特征字典表示和集成学习。具体来说，我们首先提取时间序列的均值特征和趋势特征。其次，基于特征包模式生成两类特征的直方图，并构造多个单分类器。最后，我们构建了一个集成分类器来提高分类性能。在不同的时间序列数据集上的实验结果表明，该方法与现有的方法相比具有很强的竞争力。
Time series classification is an important task for mining time series data, and many high level representations of time series have been proposed to address it. Symbolic Aggregate approXimation (SAX) is a classic high level symbolic representation method which can effectively reduce the dimensionality of time series. However, SAX-based methods for time series classification cannot achieve promising results, because SAX only extracts the mean feature of subsequence to make symbolization. In this paper, we present a novel ensemble method based on SAX called TBOPE, which is based on multi-feature dictionary representation and ensemble learning. Specifically, we first extract both the mean feature and trend feature of time series. Second, we create the histograms of two kinds of feature based on the Bag-of-Feature mode and construct multiple single classifiers. Finally, we build an ensemble classifier to improve the classification performance. Experimental results on various time series datasets have shown that the proposed method is competitive to state-of-the-art methods.