Date: 10 Nov 2012

Bayes Neutral Zone Classifiers With Applications to Nonparametric Unsupervised Settings

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Neutral zone classifiers allow for a region of neutrality when there is inadequate information to assign a predicted class with suitable confidence. A neutral zone classifier is defined by classification regions that trade off the cost of an incorrect classification against the cost of remaining neutral. In this paper, we derive a Bayes neutral zone classifier and demonstrate that it outperforms previous neutral zone classifiers with respect to the expected cost of misclassifications and also with respect to computational complexity. We apply the neutral zone classifier to a microbial community profiling application in which no training data are available, thereby illustrating how it can be extended to unsupervised settings. This article has supplementary material online.