Bayesian Learning of unobservable output aggregating multiple weak classifiers
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چکیده
Abstract: In this paper we propose a bayesian classification rule in a context where the training sample is not complete. The input X is observable, but the output Y is not, and we only utilize the result of weak classifiers instead of Y . Our method is motivated by epidemiological and bioinformatics problems. We prove the consistency of our method and illustrate its efficiency using simulations. Although up to our knowledge there are no similar algorithms for unobservable output, we compared in our simulations to supervised approaches.
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تاریخ انتشار 2013