2015
DOI: 10.1007/978-3-319-23528-8_27
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Early Classification of Time Series as a Non Myopic Sequential Decision Making Problem

Abstract: Classification of time series as early as possible is a valuable goal. Indeed, in many application domains, the earliest the decision, the more rewarding it can be. Yet, often, gathering more information allows one to get a better decision. The optimization of this time vs. accuracy tradeoff must generally be solved online and is a complex problem. This paper presents a formal criterion that expresses this trade-off in all generality together with a generic sequential meta algorithm to solve it. This meta algo… Show more

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Cited by 42 publications
(81 citation statements)
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References 9 publications
(16 reference statements)
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“…For example, in [4], the authors formalized the problem of early classification of time series as a sequential decision problem involving two costs: quality and delay of the prediction. Their method also provides the estimated time for classification, that is, how much of the remaining time series is needed to classify.…”
Section: Related Workmentioning
confidence: 99%
“…For example, in [4], the authors formalized the problem of early classification of time series as a sequential decision problem involving two costs: quality and delay of the prediction. Their method also provides the estimated time for classification, that is, how much of the remaining time series is needed to classify.…”
Section: Related Workmentioning
confidence: 99%
“…Recently, Dachraoui et al [3] introduced a framework for cost-aware early classification of time series that is dedicated to optimizing a trade-o↵ between the accuracy of the prediction and the time at which it is performed. This framework hence involves both costs defined above: a misclassification cost and a cost of delaying the prediction.…”
Section: Introductionmentioning
confidence: 99%
“…This framework hence involves both costs defined above: a misclassification cost and a cost of delaying the prediction. The authors of [3] derive an algorithm that decides, at test time, whether the decision should be made at a given time instant t or if more data should be waited for (based on both costs defined above). In addition, the method of [3] has two other interesting properties : it is adaptive and nonmyopic.…”
Section: Introductionmentioning
confidence: 99%
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