Accession Number : ADA323712
Title : Decision Boundary Analysis Feature Selection for Breast Cancer Diagnosis.
Descriptive Note : Master's thesis,
Corporate Author : AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH SCHOOL OF ENGINEERING
Personal Author(s) : Gregg, Daniel W.
PDF Url : ADA323712
Report Date : MAR 1997
Pagination or Media Count : 83
Abstract : 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.
Descriptors : *BREAST CANCER, RATIOS, NEURAL NETS, DECISION MAKING, EIGENVECTORS, THESES, EIGENVALUES, VARIABLES, DIAGNOSIS(MEDICINE), CORRELATION, CLASSIFICATION, PATTERN RECOGNITION, EXTRACTION, SELECTION.
Subject Categories : Medicine and Medical Research
Distribution Statement : APPROVED FOR PUBLIC RELEASE