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Early classification of multivariate temporal observations by extraction of interpretable shapelets.

Ghalwash MF, Obradovic Z - BMC Bioinformatics (2012)

Bottom Line: Early classification of time series is beneficial for biomedical informatics problems such including, but not limited to, disease change detection.In addition, extracting patterns from the original time series helps domain experts to gain insights into the classification results.The time series were classified by searching for the earliest closest patterns.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia, USA. zoran.obradovic@temple.edu.

ABSTRACT

Background: 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.

Results: 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.

Conclusion: 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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Related in: MedlinePlus

Illustration of the distance threshold. The distance threshold is chosen such that it divides the dataset into two separate groups (red and blue groups). It is clear that there is no unique best threshold. Any threshold between 10 and 14 or between 16 and 21 has only either one false negative or one false positive. However, there is no perfect threshold that separates the datasets into two pure groups.
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Figure 2: Illustration of the distance threshold. The distance threshold is chosen such that it divides the dataset into two separate groups (red and blue groups). It is clear that there is no unique best threshold. Any threshold between 10 and 14 or between 16 and 21 has only either one false negative or one false positive. However, there is no perfect threshold that separates the datasets into two pure groups.

Mentions: • The distance dibetween s and every time series Ti in the dataset is computed using Equation 1. The distance di is represented as a point in the order line as shown in Figure 2. If Class(Ti) = cf, then di is represented as blue point. If Class(Ti) ≠ cf, then di is represented as red square.


Early classification of multivariate temporal observations by extraction of interpretable shapelets.

Ghalwash MF, Obradovic Z - BMC Bioinformatics (2012)

Illustration of the distance threshold. The distance threshold is chosen such that it divides the dataset into two separate groups (red and blue groups). It is clear that there is no unique best threshold. Any threshold between 10 and 14 or between 16 and 21 has only either one false negative or one false positive. However, there is no perfect threshold that separates the datasets into two pure groups.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3475011&req=5

Figure 2: Illustration of the distance threshold. The distance threshold is chosen such that it divides the dataset into two separate groups (red and blue groups). It is clear that there is no unique best threshold. Any threshold between 10 and 14 or between 16 and 21 has only either one false negative or one false positive. However, there is no perfect threshold that separates the datasets into two pure groups.
Mentions: • The distance dibetween s and every time series Ti in the dataset is computed using Equation 1. The distance di is represented as a point in the order line as shown in Figure 2. If Class(Ti) = cf, then di is represented as blue point. If Class(Ti) ≠ cf, then di is represented as red square.

Bottom Line: Early classification of time series is beneficial for biomedical informatics problems such including, but not limited to, disease change detection.In addition, extracting patterns from the original time series helps domain experts to gain insights into the classification results.The time series were classified by searching for the earliest closest patterns.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia, USA. zoran.obradovic@temple.edu.

ABSTRACT

Background: 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.

Results: 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.

Conclusion: 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.

Show MeSH
Related in: MedlinePlus