Reference : Segment and combine approach for non-parametric time-series classification
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/9359
Segment and combine approach for non-parametric time-series classification
English
Geurts, Pierre mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Wehenkel, Louis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
2005
Lecture Notes in Computer Science
Springer-Verlag Berlin
3721
Knowledge Discovery in Databases: Pkdd 2005
478-485
Yes
International
0302-9743
Berlin
[en] This paper presents a novel, generic, scalable, autonomous, and flexible supervised learning algorithm for the classification of multivariate and variable length time series. The essential ingredients of the algorithm are randomization, segmentation of time-series, decision tree ensemble based learning of subseries classifiers, combination of subseries classification by voting, and cross-validation based temporal resolution adaptation. Experiments are carried out with this method on 10 synthetic and real-world datasets. They highlight the good behavior of the algorithm on a large diversity of problems. Our results are also highly competitive with existing approaches from the literature.
http://hdl.handle.net/2268/9359

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