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

ABSTRACT:

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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

SUBMITTER: Ghalwash MF 

PROVIDER: S-EPMC3475011 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature