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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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Comparison of the MSD method to the baseline classifier. The performance of 1NN with DTW using different time series length and MSD on the viral infection datasets. The left (right) group shows accuracy of the classifiers on H3N2 (HRV) dataset, respectively. The x-axis within a group is ordered by the fraction of the time series, shown in parenthesis. The results provide evidence that the MSD method is more accurate than 1NN.
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Figure 7: Comparison of the MSD method to the baseline classifier. The performance of 1NN with DTW using different time series length and MSD on the viral infection datasets. The left (right) group shows accuracy of the classifiers on H3N2 (HRV) dataset, respectively. The x-axis within a group is ordered by the fraction of the time series, shown in parenthesis. The results provide evidence that the MSD method is more accurate than 1NN.

Mentions: We constructed 2 datasets out of H3N2, which we call 1NN(70) and 1NN(60). We also constructed 2 datasets out of the HRV dataset, which we call 1NN(50) and 1NN(40). The 1NN(k) dataset was constructed from the prefixes of the original dataset such that all its time series are of fraction k of the original time series. For each dataset, 1NN was applied using all genes. The results are shown in Figure 7.


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

Ghalwash MF, Obradovic Z - BMC Bioinformatics (2012)

Comparison of the MSD method to the baseline classifier. The performance of 1NN with DTW using different time series length and MSD on the viral infection datasets. The left (right) group shows accuracy of the classifiers on H3N2 (HRV) dataset, respectively. The x-axis within a group is ordered by the fraction of the time series, shown in parenthesis. The results provide evidence that the MSD method is more accurate than 1NN.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: Comparison of the MSD method to the baseline classifier. The performance of 1NN with DTW using different time series length and MSD on the viral infection datasets. The left (right) group shows accuracy of the classifiers on H3N2 (HRV) dataset, respectively. The x-axis within a group is ordered by the fraction of the time series, shown in parenthesis. The results provide evidence that the MSD method is more accurate than 1NN.
Mentions: We constructed 2 datasets out of H3N2, which we call 1NN(70) and 1NN(60). We also constructed 2 datasets out of the HRV dataset, which we call 1NN(50) and 1NN(40). The 1NN(k) dataset was constructed from the prefixes of the original dataset such that all its time series are of fraction k of the original time series. For each dataset, 1NN was applied using all genes. The results are shown in Figure 7.

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