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Aggregate Features in Multisample Classification Problems
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DOI：10.1109/JBHI.2014.2314856.png)
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Abstract
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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.
Keywords:
Bayes methods
decision support systems
machine learning
pattern analysis
statistical learning
supervised learning
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