Fuzzy pre-processing of gold standards as applied to biomedical spectra classification.
Fuzzy gold standard adjustment is a novel fuzzy set theoretic pre-processing strategy that compensates for the possible imprecision of a well-established gold standard (reference test) by adjusting, if necessary, the class labels in the design set while maintaining the gold standard's discriminatory power. The adjusted gold standard incorporates robust within-class centroid information. This strategy was applied to biomedical data acquired from a MR spectrometer for the purpose of classifying human brain neoplasms. It is shown that consistent improvement (10-13%) to the discriminatory power of the underlying classifier is obtained when using this pre-processing strategy.
Pizzi
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NJ
National Research Council Canada, Institute for Biodiagnostics, Winnipeg.
D001254:Astrocytoma / Q000145:classification / Q000473:pathology; D001932:Brain Neoplasms / Q000145:classification* / Q000473:pathology; D004827:Epilepsy / Q000145:classification / Q000473:pathology; D017143:Fuzzy Logic; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000145:classification*; D008579:Meningioma / Q000145:classification / Q000473:pathology; D016571:Neural Networks, Computer; D012016:Reference Values