Daniel Jeske, statistics professor at the University of California, Riverside, will be the featured speaker at a Department of Mathematical Sciences statistics seminar. The title of his talk is, "Neutral Zone Classifiers and their Applications."
There will be refreshments starting at 10:45 a.m.
Abstract: 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 talk, Bayes neutral zone classifiers will be discussed and it will be demonstrated that they out perform previous neutral zone classifiers with respect to the expected cost of misclassifications and also with respect to computational complexity. A neutral zone classifier is illustrated with a microbial community profiling application in which no training data is available. Previous applications of neutral zone classifiers have only dealt with the scenario where training data exists. Extensions to sequential neutral zone classification will also be discussed.