Utilizing a class labeling feature in an adaptive Bayesian classifier
Robert S. Lynch, Peter Willett · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
In this paper, the Mean-Field Bayesian Data Reduction Algorithm is developed that adaptively trains on data containing missing values. In the basic data model for this algorithm each feature vector of a given class contains a class-labeling feature. Thus, the methods developed here are used to demonstrate performance for problems in which it is desired to adapt the existing training data with data containing missing values, such as the class-labeling feature. Given that, the Mean-Field Bayesian Data Reduction Algorithm labels the adapted data, while simultaneously determining those features that provide best classification performance. That is, performance is improved by reducing the data to mitigate the effects of the curse of dimensionality. Further, to demonstrate performance, the algorithm is compared to the classifier that does not adapt and bases its decisions on only the prior training data, and also the optimal clairvoyant classifier.