Time series labeling algorithms based on the K-nearest neighbors' frequencies

Authors:
Efendi N. Nasibov;Sinem Peker
Affiliations:
Department of Computer Science, Faculty of Sciences, Dokuz Eylul University, Tinaztepe Campus, 35160 Buca, Izmir, Turkey;Department of Statistics, Faculty of Science and Letters, Yasar University, Selcuk Yasar Campus, 35100 Bornova, Izmir, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

In the current paper, time series labeling task is analyzed and some solution algorithms are presented. In these algorithms, fuzzy c-means clustering, which is one of the unsupervised learning methods, is used to obtain the labels of the time series. Then K-nearest neighborhood (KNN) rule is performed on the labels to obtain more relevant smooth intervals. As an application, the handled labeling algorithms are performed on bispectral index (BIS) data, which are time series measures of brain activity. Finally, smoothing process is found useful in the estimation of sedation stage labels.