TY - GEN
T1 - A feature segment based time series classification algorithm
AU - Pan, Liqiang
AU - Meng, Qi
AU - Pan, Wei
AU - Zhao, Yi
AU - Gao, Huijun
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/2/11
Y1 - 2016/2/11
N2 - Traditional works on time series classification usually use all of data in time series without distinction. However, that will swamp the discriminative information and decrease the correctness of classification. In this paper, a feature segment based time series classification algorithm was proposed, which only selects some highly discriminative time series data for classification. Firstly, an adaptive time series segmentation method was proposed. Then, a large margin based feature segment selection method was given. Based on these two methods, a time series classification framework was established after representing the time series with the optimal segments. By exploring the discriminative temporal patterns hidden in subsequences of time series and giving them more emphasize, the algorithm proposed in this paper can improve the time series classification performance greatly. Extensive experimental results showed that the proposed algorithm can achieve a good classification performance.
AB - Traditional works on time series classification usually use all of data in time series without distinction. However, that will swamp the discriminative information and decrease the correctness of classification. In this paper, a feature segment based time series classification algorithm was proposed, which only selects some highly discriminative time series data for classification. Firstly, an adaptive time series segmentation method was proposed. Then, a large margin based feature segment selection method was given. Based on these two methods, a time series classification framework was established after representing the time series with the optimal segments. By exploring the discriminative temporal patterns hidden in subsequences of time series and giving them more emphasize, the algorithm proposed in this paper can improve the time series classification performance greatly. Extensive experimental results showed that the proposed algorithm can achieve a good classification performance.
KW - Dynamic time warping
KW - Feature segment
KW - Large margin
KW - Nearest neighbor
KW - Time series classification
UR - https://www.scopus.com/pages/publications/84963957190
U2 - 10.1109/IMCCC.2015.286
DO - 10.1109/IMCCC.2015.286
M3 - 会议稿件
AN - SCOPUS:84963957190
T3 - Proceedings - 5th International Conference on Instrumentation and Measurement, Computer, Communication, and Control, IMCCC 2015
SP - 1333
EP - 1338
BT - Proceedings - 5th International Conference on Instrumentation and Measurement, Computer, Communication, and Control, IMCCC 2015
A2 - Li, Jun-Bao
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Instrumentation and Measurement, Computer, Communication, and Control, IMCCC 2015
Y2 - 18 September 2015 through 20 September 2015
ER -