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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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Performance of MSD method on the H3N2 dataset using different numbers of top genes. This figure illustrates the performance of the MSD method on the H3N2 dataset using different numbers of top genes from the provided ranked list [11]. Red, green, and blue lines represent coverage, relative accuracy, and accuracy, respectively.
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Figure 6: Performance of MSD method on the H3N2 dataset using different numbers of top genes. This figure illustrates the performance of the MSD method on the H3N2 dataset using different numbers of top genes from the provided ranked list [11]. Red, green, and blue lines represent coverage, relative accuracy, and accuracy, respectively.

Mentions: For the viral infection dataset, a list of 23 genes associated with the viral infection sorted by their relevance to the infection diagnosis is provided in a recently published study [11]. Starting from this list, we searched for a subset of genes that could be used to achieve more accurate results. We ran MSD using different numbers of top genes provided by the ranked list. The coverage, the relative accuracy, and the accuracy of MSD on H3N2 are shown in Figure 6. It is clear that the method becomes more accurate when using 11 genes instead of using 23 genes.


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

Ghalwash MF, Obradovic Z - BMC Bioinformatics (2012)

Performance of MSD method on the H3N2 dataset using different numbers of top genes. This figure illustrates the performance of the MSD method on the H3N2 dataset using different numbers of top genes from the provided ranked list [11]. Red, green, and blue lines represent coverage, relative accuracy, and accuracy, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Performance of MSD method on the H3N2 dataset using different numbers of top genes. This figure illustrates the performance of the MSD method on the H3N2 dataset using different numbers of top genes from the provided ranked list [11]. Red, green, and blue lines represent coverage, relative accuracy, and accuracy, respectively.
Mentions: For the viral infection dataset, a list of 23 genes associated with the viral infection sorted by their relevance to the infection diagnosis is provided in a recently published study [11]. Starting from this list, we searched for a subset of genes that could be used to achieve more accurate results. We ran MSD using different numbers of top genes provided by the ranked list. The coverage, the relative accuracy, and the accuracy of MSD on H3N2 are shown in Figure 6. It is clear that the method becomes more accurate when using 11 genes instead of using 23 genes.

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