Deep Reinforcement Learning based Triggering Function for Early Classifiers of Time Series

• URL: http://arxiv.org/abs/2502.06584v1
• Date: Mon, 10 Feb 2025 15:52:55 GMT
• Title: Deep Reinforcement Learning based Triggering Function for Early Classifiers of Time Series
• Authors: Aurélien Renault, Alexis Bondu, Antoine Cornuéjols, Vincent Lemaire,
• Abstract summary: Early Classification of Time Series (ECTS) has been recognized as an important problem in many areas where decisions have to be taken soon. Numerous approaches have been proposed, based on different triggering functions, each taking into account various pieces of information related to the incoming time series. Experiments show that the system we describe, called scAlert, significantly outperforms its state-of-theart competitors.
• Score: 0.5399800035598185
• License:
• Abstract: Early Classification of Time Series (ECTS) has been recognized as an important problem in many areas where decisions have to be taken as soon as possible, before the full data availability, while time pressure increases. Numerous ECTS approaches have been proposed, based on different triggering functions, each taking into account various pieces of information related to the incoming time series and/or the output of a classifier. Although their performances have been empirically compared in the literature, no studies have been carried out on the optimality of these triggering functions that involve ``man-tailored'' decision rules. Based on the same information, could there be better triggering functions? This paper presents one way to investigate this question by showing first how to translate ECTS problems into Reinforcement Learning (RL) ones, where the very same information is used in the state space. A thorough comparison of the performance obtained by ``handmade'' approaches and their ``RL-based'' counterparts has been carried out. A second question investigated in this paper is whether a different combination of information, defining the state space in RL systems, can achieve even better performance. Experiments show that the system we describe, called \textsc{Alert}, significantly outperforms its state-of-the-art competitors on a large number of datasets.

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