Sequential selection of discrete features for neural networks - A Bayesian approach to building a cascade

A feature selection procedure is used to successively remove features one-by-one from a statistical classifier by an iterative backward search. Each classifier uses a smaller subset of features than the classifier in the previous iteration. The classifiers are subsequently combined into a cascade. Each classifier in the cascade should classify cases to which a reliable class label can be assigned. Other cases should be propagated to the next classifier which uses also the value of a new feature. Experiments demonstrate the feasibility of building cascades of classifiers (neural networks for prediction of atrial fibrillation (FA)) using a backward search scheme for feature selection. (C) 1999 Elsevier Science B.V. All rights reserved.