Cost-Aware Early Classification of Time Series

Romain Tavenard 1, 2 Simon Malinowski 3
1 OBELIX - Environment observation with complex imagery
UBS - Université de Bretagne Sud, IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
2 LETG - Rennes - Littoral, Environnement, Télédétection, Géomatique
LETG - Littoral, Environnement, Télédétection, Géomatique
3 LinkMedia - Creating and exploiting explicit links between multimedia fragments
Inria Rennes – Bretagne Atlantique , IRISA-D6 - MEDIA ET INTERACTIONS
Abstract : In time series classification, two antagonist notions are at stake. On the one hand, in most cases, the sooner the time series is classified , the more rewarding. On the other hand, an early classification is more likely to be erroneous. Most of the early classification methods have been designed to take a decision as soon as sucient level of reliability is reached. However, in many applications, delaying the decision with no guarantee that the reliability threshold will be met in the future can be costly. Recently, a framework dedicated to optimizing a trade-off between classification accuracy and the cost of delaying the decision was proposed, together with an algorithm that decides online the optimal time instant to classify an incoming time series. On top of this framework , we build in this paper two di↵erent early classification algorithms that optimize a trade-off between decision accuracy and the cost of delaying the decision. These algorithms are non-myopic in the sense that, even when classification is delayed, they can provide an estimate of when the optimal classification time is likely to occur. Our experiments on real datasets demonstrate that the proposed approaches are more robust than existing methods.
Type de document :
Communication dans un congrès
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery, Sep 2016, Riva del Garda, Italy. pp.632-647, 〈10.1007/978-3-319-46128-1_40〉
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https://halshs.archives-ouvertes.fr/halshs-01339007
Contributeur : Romain Tavenard <>
Soumis le : mercredi 29 juin 2016 - 14:24:26
Dernière modification le : lundi 25 septembre 2017 - 10:06:19

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Romain Tavenard, Simon Malinowski. Cost-Aware Early Classification of Time Series. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery, Sep 2016, Riva del Garda, Italy. pp.632-647, 〈10.1007/978-3-319-46128-1_40〉. 〈halshs-01339007〉

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