Early classification of multivariate temporal observations by extraction of interpretable shapelets
Open Access
- 8 August 2012
- journal article
- research article
- Published by Springer Science and Business Media LLC in BMC Bioinformatics
- Vol. 13 (1), 195
- https://doi.org/10.1186/1471-2105-13-195
Abstract
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.Keywords
This publication has 13 references indexed in Scilit:
- Logical-shapeletsPublished by Association for Computing Machinery (ACM) ,2011
- Temporal Pattern Mining for Multivariate Time Series ClassificationJournal of Medical Imaging and Health Informatics, 2011
- Extracting Interpretable Features for Early Classification on Time SeriesPublished by Society for Industrial & Applied Mathematics (SIAM) ,2011
- Gene Expression Signatures Diagnose Influenza and Other Symptomatic Respiratory Viral Infections in HumansCell Host & Microbe, 2009
- Constrained mixture estimation for analysis and robust classification of clinical time seriesBioinformatics, 2009
- Querying and mining of time series dataProceedings of the VLDB Endowment, 2008
- Alignment and classification of time series gene expression in clinical studiesBioinformatics, 2008
- Classification of Multivariate Time Series and Structured Data Using Constructive InductionMachine Learning, 2005
- Transcription-Based Prediction of Response to IFNβ Using Supervised Computational MethodsPLoS Biology, 2004
- Model Selection with Cross-Validations and Bootstraps — Application to Time Series Prediction with RBFN ModelsLecture Notes in Computer Science, 2003