Article (Scientific journals)
Segment and combine approach for non-parametric time-series classification
Geurts, Pierre; Wehenkel, Louis
2005In Lecture Notes in Computer Science, 3721, p. 478-485
Peer reviewed
 

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Abstract :
[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.
Disciplines :
Computer science
Author, co-author :
Geurts, Pierre  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Wehenkel, Louis  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Segment and combine approach for non-parametric time-series classification
Publication date :
2005
Journal title :
Lecture Notes in Computer Science
ISSN :
0302-9743
eISSN :
1611-3349
Publisher :
Springer-Verlag Berlin, Berlin, Germany
Special issue title :
Knowledge Discovery in Databases: Pkdd 2005
Volume :
3721
Pages :
478-485
Peer reviewed :
Peer reviewed
Available on ORBi :
since 22 March 2009

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