The usefulness of two-class statistical classifiers is limited when one or both of the conditional miss-classification rates are unacceptably high. Incorporating a neutral zone region into the classifier provides a mechanism to refer ambiguous cases to follow-up where additional information might be obtained to clarify the classification decision. Through the use of the neutral zone region, the conditional miss-classification rates can be controlled and the classifier becomes useful. Real-life examples, including applications to gastric and prostate cancer, are used to illustrate how neutral zone regions can extract utility from disappointing classifiers that might otherwise be abandoned.

Prof. Daniel Jeske