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

被引：5

作者：

Nasibov, Efendi N. ^{[1
]}

Peker, Sinem ^{[2
]}

机构：

[1] Dokuz Eylul Univ, Fac Sci, Dept Comp Sci, TR-35160 Izmir, Turkey

[2] Yasar Univ, Fac Sci & Letters, Dept Stat, TR-35100 Izmir, Turkey

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2011年 / 38卷 / 05期

关键词：

Time series; Clustering; FCM; K-nearest neighbor; Bispectral index; CLUSTER VALIDITY; MODEL;

D O I：

10.1016/j.eswa.2010.09.147

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：5028 / 5035

页数：8

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