Overriding the Experts: A Stacking Method for Combining Marginal Classifiers

The design of an optimal Bayesian classifier for multiple features is dependent on the estimation of multidimensional joint probability density functions and therefore requires a design sample size that increases exponentially with the number of dimensions. A method was developed that combines classifications from marginal density functions using an additional classifier. Unlike voting methods, this method can select a more appropriate class than the ones selected by the marginal classifiers, thus "overriding" their decisions. For two classes and two features, this method always demonstrates a probability of error no worse than the probability of error of the best marginal classifier.

• Type: Conference
• Year: 2000
• Where: FLAIRS
• Authors: Mark D. Happel, Peter Bock
• Keywords: Artificial Intelligence, Density Functions, FLAIRS 2000, Marginal Classifier, Optimal Bayesian Classifier