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Conference papers

Generalized chronicles for temporal sequence classification

Yann Dauxais 1 Thomas Guyet 2, 3
3 LACODAM - Large Scale Collaborative Data Mining
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
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.
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https://hal.inria.fr/hal-03025590
Contributor : Thomas Guyet <>
Submitted on : Thursday, November 26, 2020 - 12:34:35 PM
Last modification on : Thursday, January 7, 2021 - 4:31:56 PM

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  • HAL Id : hal-03025590, version 1

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Yann Dauxais, Thomas Guyet. Generalized chronicles for temporal sequence classification. Advanced Analytics and Learning on Temporal Data, Sep 2020, Ghent, Belgium. ⟨hal-03025590⟩

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