Classification Rules that Include Neutral Zones and Their Application to Microbial Community Profiling

We extend the classical one-dimensional Bayes binary classifier to create a new classification rule that has a region of neutrality to account for cases where the implied weight of evidence is too weak for a confident classification. Our proposed rule allows a “No Prediction” when the observation is too ambiguous to have confidence in a definite prediction. The motivation for making “No Prediction” is that in our microbial community profiling application, a wrong prediction can be worse than making no prediction at all. On the other hand, too many “No Predictions” have adverse implications as well. Consequently, our proposed rule incorporates this trade-off using a cost structure that weighs the penalty for not making a definite prediction against the penalty for making an incorrect definite prediction. We demonstrate that our proposed rule outperforms a naive neutral-zone rule that has been routinely used in biological applications similar to ours.