Speaker
Prof.
Daniel Jeske
(University of California, Riverside)
Description
The use of a statistical classifier can be limited by its conditional misclassification rates (i.e., false positive rate and false negative rate) even when the overall misclassification rate is satisfactory. When one or both conditional misclassification rates are high, a neutral zone can be introduced to lower and possibly balance these rates. In this talk the need for neutral zones will be motivated and a method for constructing neutral zones will be explained. Real-life applications of neutral zone classifiers to prostate cancer diagnosis and to student evaluations of teaching will be discussed.
| Type of presentation | Talk |
|---|---|
| Classification | Both methodology and application |
| Keywords | Statistical classifier, Neutral zone, Conditional misclassification rates |
Primary author
Prof.
Daniel Jeske
(University of California, Riverside)