Abstract :
The only guaranteed technique for choosing the best subset of N properties from a set of M is to try all (MN) possible combinations. This is computationally impractical for sets of even moderate size, so heuristic techniques are required. This paper presents seven techniques for choosing good subsets of properties and compares their performance on a nine-class vectorcardiogram classification problem.
Keywords :
Classification techniques, dimensionality reduction, electrocardiogram classification, feature extraction, Karhunen-Loéve expansion, multivariate data analysis, pattern recognition, principal components, property selection, statistical decision making.; Biomedical engineering; Data analysis; Decision making; Error analysis; Feature extraction; Military computing; Pattern classification; Pattern recognition; Physiology; Principal component analysis; Classification techniques, dimensionality reduction, electrocardiogram classification, feature extraction, Karhunen-Loéve expansion, multivariate data analysis, pattern recognition, principal components, property selection, statistical decision making.;
