A time series forest for classification and feature extraction
A time series forest for classification and feature extraction
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DOI：
10.1016/j.ins.2013.02.030
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发表时间：
2013-08-01
影响因子：
8.1
通讯作者：
Vladimir, Martyanov
中科院分区：
文献类型：
--
作者：
Deng, Houtao;Runger, George;Vladimir, Martyanov
提出了一种基于树集成的时间序列分类方法--时间序列森林（TSF）。TSF采用熵增益和距离测量的组合，称为入口（熵和距离）增益，用于评估分裂。实验研究表明，引入入口增益后，TSF的精度得到了提高. TSF在每个树节点上随机采样特征，计算复杂度与时间序列的长度成线性关系，可以使用并行计算技术来构建。提出了时间重要性曲线来捕获对分类有用的时间特征。实验研究表明，TSF使用简单的功能，如平均值，标准差和斜率是计算效率高，优于强大的竞争对手，如一个最近邻分类器与动态时间弯曲。(C)2013 Elsevier Inc. All rights reserved.
A tree-ensemble method, referred to as time series forest (TSF), is proposed for time series classification. TSF employs a combination of entropy gain and a distance measure, referred to as the Entrance (entropy and distance) gain, for evaluating the splits. Experimental studies show that the Entrance gain improves the accuracy of TSF. TSF randomly samples features at each tree node and has computational complexity linear in the length of time series, and can be built using parallel computing techniques. The temporal importance curve is proposed to capture the temporal characteristics useful for classification. Experimental studies show that TSF using simple features such as mean, standard deviation and slope is computationally efficient and outperforms strong competitors such as one-nearest-neighbor classifiers with dynamic time warping. (C) 2013 Elsevier Inc. All rights reserved.