Performance Analysis of Sequential Probability Ratio Test

Li, Yu; Li, X. Rong

doi:10.1080/07474946.2013.843329

Cited by 5 publications

(1 citation statement)

References 41 publications

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“…) is computed and compared with two thresholds that are set according to the required error of the first kind (false positive error) and error of the second kind (false negative error). Extensions to non-stationary distributions have been put forward (see Liu and Li 2013;Novikov 2008).…”

Section: Related Workmentioning

confidence: 99%

Early classification of time series

et al. 2021

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An increasing number of applications require to recognize the class of an incoming time series as quickly as possible without unduly compromising the accuracy of the prediction. In this paper, we put forward a new optimization criterion which takes into account both the cost of misclassification and the cost of delaying the decision. Based on this optimization criterion, we derived a family of non-myopic algorithms which try to anticipate the expected future gain in information in balance with the cost of waiting. In one class of algorithms, unsupervised-based, the expectations use the clustering of time series, while in a second class, supervised-based, time series are grouped according to the confidence level of the classifier used to label them. Extensive experiments carried out on real datasets using a large range of delay cost functions show that the presented algorithms are able to solve the earliness vs. accuracy trade-off, with the supervised partition based approaches faring better than the unsupervised partition based ones. In addition, all these methods perform better in a wide variety of conditions than a state of the art method based on a myopic strategy which is recognized as being very competitive. Furthermore, our experiments show that the non-myopic feature of the proposed approaches explains in large part the obtained performances.

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Section: Related Workmentioning

confidence: 99%