Identification of signatures in biomedical spectra using domain knowledge.
OBJECTIVE
Demonstrate that incorporating domain knowledge into feature selection methods helps identify interpretable features with predictive capability comparable to a state-of-the-art classifier.


METHODS
Two feature selection methods, one using a genetic algorithm (GA) the other a L(1)-norm support vector machine (SVM), were investigated on three real-world biomedical magnetic resonance (MR) spectral datasets of increasing difficulty. Consensus sets of the feature sets obtained by the two methods were also assessed.


CONCLUSIONS
Features identified independently by the two methods and by their consensus, determine class-discriminatory groups or individual features, whose predictive power compares favorably with that of a state-of-the-art classifier. Furthermore, the identified feature signatures form stable groupings at definite spectral positions, hence are readily interpretable. This is a useful and important practical result for generating hypothesis for the domain expert.
Pranckeviciene
Erinija
E
Institute for Biodiagnostics, National Research Council, 435 Ellice Avenue, Winnipeg, Manitoba, Canada R3B 1Y6.
Somorjai
Ray
R
Baumgartner
Richard
R
Jeon
Moon-Gu
MG
D000465:Algorithms; D001185:Artificial Intelligence; D001826:Body Fluids; D002176:Candida albicans / Q000145:classification; D041022:Candida tropicalis / Q000145:classification; D006801:Humans