Abstract
Early classification approaches deal with the problem of reliably labeling incomplete time series as soon as possible given a level of confidence. While developing new approaches for this problem has been getting increasing attention recently, their evaluation are still not thoroughly considered. In this article, we propose a new evaluation protocol for early classifiers. This protocol is generic and does not depend on the criteria used to evaluate the classifiers. Our protocol is successfully applied to 23 publicly available data sets.
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Dachraoui, A., Bondu, A., Cornuéjols, A. (2014). Evaluation Protocol of Early Classifiers over Multiple Data Sets. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8835. Springer, Cham. https://doi.org/10.1007/978-3-319-12640-1_66
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DOI: https://doi.org/10.1007/978-3-319-12640-1_66
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