Decision Boundary Analysis Feature Selection for Breast Cancer Diagnosis

The general pattern recognition problem always involves the extraction of features to be used in pattern classification. There are no theoretical limitations to the number of features which can be obtained for a given pattern recognition problem. This research will develop a correlation procedure for screening a large feature set without the use of a trained classifier. The results will be compared to established saliency metrics such as the Fisher ratio and derivative-based techniques such as Ruck's saliency.Air Force Inst of Tech Wright-Patterson AFB OH School of Engineering is the author of 'Decision Boundary Analysis Feature Selection for Breast Cancer Diagnosis', published 1997 under ISBN 9781423567707 and ISBN 1423567706.