Early classification of multivariate temporal observations by extraction of interpretable shapelets.
Early classification of multivariate temporal observations by extraction of interpretable shapelets.
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DOI：
10.1186/1471-2105-13-195
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发表时间：
2012-08-08
影响因子：
3
通讯作者：
Obradovic Z
中科院分区：
文献类型：
--
作者：
Ghalwash MF;Obradovic Z
时间序列的早期分类有利于生物医学信息学问题，包括但不限于疾病变化检测。早期分类可以在疾病完全形成之前识别疾病的发作，从而提供巨大的帮助。此外，从原始时间序列中提取模式有助于领域专家深入了解分类结果。最近使用称为 shapelet 的时间序列段研究了这个问题。在本文中，我们提出了一种称为多元 Shapelet 检测 (MSD) 的方法，该方法允许对多元时间序列进行早期和患者特定的分类。该方法从时间序列的所有维度中提取时间序列模式（称为多元 shapelet），这些维度在本地清楚地体现了目标类。通过搜索最早最接近的模式对时间序列进行分类。所提出的多变量时间序列的早期分类方法已经在人类病毒感染和药物反应研究的八个基因表达数据集上进行了评估。在我们的实验中，MSD 方法优于基线方法，仅使用 40%-64% 的时间序列即可实现高精度分类。获得的结果证明，在短时间序列上使用传统的分类方法不如使用专门用于早期分类的方法准确。对于早期分类任务，我们提出了一种称为多元 Shapelet 检测 (MSD) 的方法，该方法从时间序列的所有维度中提取模式。我们表明，MSD 方法可以通过使用少至 40%-64% 的时间序列长度来对时间序列进行早期分类。
Early classification of time series is beneficial for biomedical informatics problems such including, but not limited to, disease change detection. Early classification can be of tremendous help by identifying the onset of a disease before it has time to fully take hold. In addition, extracting patterns from the original time series helps domain experts to gain insights into the classification results. This problem has been studied recently using time series segments called shapelets. In this paper, we present a method, which we call Multivariate Shapelets Detection (MSD), that allows for early and patient-specific classification of multivariate time series. The method extracts time series patterns, called multivariate shapelets, from all dimensions of the time series that distinctly manifest the target class locally. The time series were classified by searching for the earliest closest patterns. The proposed early classification method for multivariate time series has been evaluated on eight gene expression datasets from viral infection and drug response studies in humans. In our experiments, the MSD method outperformed the baseline methods, achieving highly accurate classification by using as little as 40%-64% of the time series. The obtained results provide evidence that using conventional classification methods on short time series is not as accurate as using the proposed methods specialized for early classification. For the early classification task, we proposed a method called Multivariate Shapelets Detection (MSD), which extracts patterns from all dimensions of the time series. We showed that the MSD method can classify the time series early by using as little as 40%-64% of the time series’ length.
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影响因子：
--
作者：
Dua, Sumeet;Saini, Sheetal;Singh, Harpreet
通讯作者：
Singh, Harpreet
DOI：
10.1093/bioinformatics/btn152
发表时间：
2008-07-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Lin TH;Kaminski N;Bar-Joseph Z
通讯作者：
Bar-Joseph Z
DOI：
10.1093/bioinformatics/btp222
发表时间：
2009-06-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Costa IG;Schönhuth A;Hafemeister C;Schliep A
通讯作者：
Schliep A
影响因子：
7.5
作者：
Kadous, MW;Sammut, C
通讯作者：
Sammut, C
影响因子：
9.8
作者：
Baranzini SE;Mousavi P;Rio J;Caillier SJ;Stillman A;Villoslada P;Wyatt MM;Comabella M;Greller LD;Somogyi R;Montalban X;Oksenberg JR
通讯作者：
Oksenberg JR