DocumentCode :
2915306
Title :
Extracting meaningful patterns for time series classification
Author :
Zhang, Xiao-hang ; Wu, Jun ; Yang, Xue-cheng ; Lu, Ting-jie
Author_Institution :
Econ. & Manage. Sch., Beijing Univ. of Posts & Telecommun., Beijing
fYear :
2008
fDate :
1-6 June 2008
Firstpage :
2513
Lastpage :
2516
Abstract :
An import area in machine learning is multivariate time series classification. In this paper we present a novel algorithm which extracts some meaningful patterns from time series data and then uses traditional machine learning algorithm to create classifier. During the stage of pattern extraction, the Gird function is used to evaluate the patterns and the starting position and the length of each pattern are automatically determined. We also apply sampling method to reduce the search space and improve efficiency. The common datasets are used to check our algorithm which is compared with the naive algorithms. The results show that a lot of improvement can be gained in terms of interpretability, simplicity of the model and also in terms of accuracy.
Keywords :
learning (artificial intelligence); pattern classification; time series; machine learning; meaningful pattern extraction; multivariate time series classification; time series data; Evolutionary computation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1822-0
Electronic_ISBN :
978-1-4244-1823-7
Type :
conf
DOI :
10.1109/CEC.2008.4631135
Filename :
4631135
Link To Document :
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