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Communication Dans Un Congrès Année : 2016

A novel algorithm for online classification of time series when delaying decision is costly

Résumé

Aiding to make decisions as early as possible by learning from past experiences is becoming increasingly important in many application domains. In these settings , information can be gained by waiting for more evidences to arrive, thus helping to make better decisions that incur lower misclassification costs, but, meanwhile, the cost associated with delaying the decision generally increases, rendering the decision less attractive. Learning requires then to solve an optimization problem combining two types of competing costs. In the growing literature on online decision making , very few works have explicitly incorporated the cost of delay in the decision procedure. One recent work [DBC15] has introduced a general formalization of this optimization problem. However, the algorithm presented there to solve it is based on a clustering step, with all the attendant necessary choices of parameters that can heavily impact the results. In this paper, we adopt the same conceptual framework but we present a more direct technique involving only one parameter and lower computational demands. Extensive experimental comparisons between the two methods on synthetic and real data sets show the superiority of our method when the classification of the incomplete time series is difficult, which corresponds to a large fraction of the applications.
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Dates et versions

hal-01557210 , version 1 (05-07-2017)

Identifiants

  • HAL Id : hal-01557210 , version 1
  • PRODINRA : 396720

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Nabli Asma, Antoine Cornuéjols, Alexis Bondu. A novel algorithm for online classification of time series when delaying decision is costly. CAP'2016 Conférence francophone sur l'Apprentissage Automatique, Jul 2016, Marseille, France. ⟨hal-01557210⟩
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