[en] In this paper, we propose some new tools to allow machine learning classifiers to cope with time series data. We first argue that many time-series classification problems can be solved by detecting and combining local properties or patterns in time series. Then, a technique is proposed to find patterns which are useful for classification. These patterns are combined to build interpretable classification rules. Experiments, carried out on several artificial and real problems, highlight the interest of the approach both in terms of interpretability and accuracy of the induced classifiers.
Disciplines :
Computer science
Author, co-author :
Geurts, Pierre ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Pattern extraction for time-series classification
Publication date :
2001
Event name :
5th European Conference on Principles of Data Mining and Knowledge Discovery
Event place :
Freiburg, Germany
Event date :
2001
Audience :
International
Main work title :
Proceedings of PKDD 2001, 5th European Conference on Principles of Data Mining and Knowledge Discovery
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