.png){kind=icon}
.png){kind=icon}
Return
Classification trees for time series
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2011.08.018.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes an extension of classification trees to time series input variables. A new split criterion based on time series proximities is introduced. First, the criterion relies on an adaptive (i.e., parameterized) time series metric to cover both behaviors and values proximities. The metrics parameters may change from one internal node to another to achieve the best bisection of the set of time series. Second, the criterion involves the automatic extraction of the most discriminating subsequences. The proposed time series classification tree is applied to a wide range of datasets: public and new, real and synthetic, univariate and multivariate data. We show, through the experiments performed in this study, that the proposed tree outperforms temporal trees using standard time series distances and performs well compared to other competitive time series classifiers. (C) 2011 Elsevier Ltd. All rights reserved.
Keywords:
Time series proximity measures
Supervised classification
Classification trees
Learning metric
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W