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

共 50 条

[41] On the stopping criteria for k-Nearest Neighbor in positive unlabeled time series classification problems
Gonzalez, Mabel
Bergmeir, Christoph
Triguero, Isaac
Rodriguez, Yanet
Benitez, Jose M.
INFORMATION SCIENCES, 2016, 328 : 42 - 59
[42] Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors
Xie, Juanying
Gao, Hongchao
Xie, Weixin
Liu, Xiaohui
Grant, Philip W.
INFORMATION SCIENCES, 2016, 354 : 19 - 40
[43] PL-kNN: A Python']Python-based implementation of a parameterless k-Nearest Neighbors classifier
Jodas, Danilo Samuel
Passos, Leandro Aparecido
Adeel, Ahsan
Papa, Joao Paulo
SOFTWARE IMPACTS, 2023, 15
[44] A novel density peaks clustering algorithm for automatic selection of clustering centers based on K-nearest neighbors
Wang, Zhihe
Wang, Huan
Du, Hui
Chen, Shiyin
Shi, Xinxin
MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2023, 20 (07) : 11875 - 11894
[45] Multivariate Outlier Detection Approach Based on k-Nearest Neighbors and Its Application for Chemical Process Data
Dong, Yaming
Yan, Xuefeng
JOURNAL OF CHEMICAL ENGINEERING OF JAPAN, 2014, 47 (12) : 876 - 886
[46] Using python']python libraries and k-Nearest neighbors algorithms to delineate syn-sedimentary faults in sedimentary porous media
Martin-Martin, Manuel
Bullejos, Manuel
Cabezas, David
Alcala, Francisco Javier
MARINE AND PETROLEUM GEOLOGY, 2023, 153
[47] A new adaptation method based on adaptability under k-nearest neighbors for case adaptation in case-based design
Qi, Jin
Hu, Jie
Peng, Yinghong
EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (07) : 6485 - 6502
[48] Comparing Distance Metrics for Rotation Using the k-Nearest Neighbors Algorithm for Entropy Estimation
Huggins, David J.
JOURNAL OF COMPUTATIONAL CHEMISTRY, 2014, 35 (05) : 377 - 385
[49] Adapted K-Nearest Neighbors for Detecting Anomalies on Spatio-Temporal Traffic Flow
Djenouri, Youcef
Belhadi, Asma
Lin, Jerry Chun-Wei
Cano, Alberto
IEEE ACCESS, 2019, 7 : 10015 - 10027
[50] Multi-Style License Plate Recognition System using K-Nearest Neighbors
Park, Soungsill
Yoon, Hyoseok
Park, Seho
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2019, 13 (05): : 2509 - 2528

← 1 2 3 4 5 →