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. E...

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Bibliographic Details
Published in:Pattern recognition letters 1999-11, Vol.20 (11), p.1439-1448
Main Authors: Egmont-Petersen, M., Dassen, W.R.M., Reiber, J.H.C.
Format: Article
Language:English
Subjects:
Atrial fibrillation
Backward search
Cascade
Feature assessment
Feature selection
Pruning
Rejection
Sequential classification
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Summary: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.
ISSN:0167-8655
1872-7344
DOI:10.1016/S0167-8655(99)00112-9