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Generalized Chronicles for Temporal Sequence Classification

Thomas Guyet 1, 2 Yann Dauxais 3
1 Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement
2 LACODAM - Large Scale Collaborative Data Mining
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
3 Department of Computer Science - K.U.Leuven
Abstract : Discriminant chronicle mining (DCM) tackles temporal sequence classification by combining machine learning and chronicle mining algorithms. A chronicle is a set of events related by temporal boundaries on the delay between event occurrences. Such temporal constraints are poorly expressive and discriminant chronicles may lack of accuracy. This article generalizes discriminant chronicle mining by modeling complex temporal constraints. We present the generalized model and we instantiate different generalized chronicle models. The accuracy of these models are compared with each other on simulated and real datasets.
Keywords : Temporal patterns discriminant patterns sequence classification
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https://hal.inria.fr/hal-03090211
Contributor : Thomas Guyet <>
Submitted on : Tuesday, December 29, 2020 - 1:53:50 PM
Last modification on : Friday, January 8, 2021 - 3:40:07 AM

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  • HAL Id : hal-03090211, version 1

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Thomas Guyet, Yann Dauxais. Generalized Chronicles for Temporal Sequence Classification. Extended articles of the International Workshop on Advanced Analytics and Learning on Temporal Data, 12588, Springer, 2020, Lecture Notes in Computer Science. ⟨hal-03090211⟩

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