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• W2049426044 abstract "This paper evaluates the classification of multisample problems, such as electromyographic (EMG) data, by making aggregate features available to a per-sample classifier. It is found that the accuracy of this approach is superior to that of traditional methods such as majority vote for this problem. The classification improvements of this method, in conjunction with a confidence measure expressing the per-sample probability of classification failure (i.e., a hazard function) is described and measured. Results are expected to be of interest in clinical decision support system development." @default.
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• W2049426044 date "2015-03-01" @default.
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• W2049426044 title "Aggregate Features in Multisample Classification Problems" @default.
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• W2049426044 doi "https://doi.org/10.1109/jbhi.2014.2314856" @default.
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