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- W2039459330 abstract "Early classification of time-series data in a dynamic environment is a challenging problem of great importance in signal processing. This paper proposes a classifier architecture with a reject option capable of online decision making without the need to wait for the entire time series signal to be present. The main idea is to classify an odor/gas signal with an acceptable accuracy as early as possible. Instead of using posterior probability of a classifier, the proposed method uses the “agreement” of an ensemble to decide whether to accept or reject the candidate label. The introduced algorithm is applied to the bio-chemistry problem of odor classification to build a novel Electronic-Nose called Forefront-Nose. Experimental results on wind tunnel test-bed facility confirms the robustness of the forefront-nose compared to the standard classifiers from both earliness and recognition perspectives." @default.
- W2039459330 created "2016-06-24" @default.
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- W2039459330 date "2013-04-01" @default.
- W2039459330 modified "2023-10-12" @default.
- W2039459330 title "Classifiers with a reject option for early time-series classification" @default.
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- W2039459330 doi "https://doi.org/10.1109/ciel.2013.6613134" @default.
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