关键词：
feature extraction
Gaussian distribution
image classification
medical image processing
principal component analysis
support vector machines
COMPUTER-AIDED DIAGNOSIS
SUPPORT VECTOR MACHINES
NEURAL-NETWORK CLASSIFIERS
PATTERN-RECOGNITION
CHEST RADIOGRAPHS
PERFORMANCE
SCHEMES
摘要：
Methods: Three feature selection techniques, the stepwise feature selection (SFS), sequential floating forward search (SFFS), and principal component analysis (PCA), and two commonly used classifiers, Fisher's linear discriminant analysis (LDA) and support vector machine (SVM), were investigated. Samples were drawn from multidimensional feature spaces of multivariate Gaussian distributions with equal or unequal covariance matrices and unequal means, and with equal covariance matrices and unequal means estimated from a clinical data set. Classifier performance was quantified by the area under the receiver operating characteristic curve A(z). The mean A(z) values obtained by resubstitution and hold-out methods were evaluated for training sample sizes ranging from 15 to 100 per class. The number of simulated features available for selection was chosen to be 50, 100, and 200. Results: It was found that the relative performance of the different combinations of classifier and feature selection method depends on the feature space distributions, the dimensionality, and the available training sample sizes. The LDA and SVM with radial kernel performed similarly for most of the conditions evaluated in this study, although the SVM classifier showed a slightly higher hold-out performance than LDA for some conditions and vice versa for other conditions. PCA was comparable to or better than SFS and SFFS for LDA at small samples sizes, but inferior for SVM with polynomial kernel. For the class distributions simulated from clinical data, PCA did not show advantages over the other two feature selection methods. Under this condition, the SVM with radial kernel performed better than the LDA when few training samples were available, while LDA performed better when a large number of training samples were available. Conclusions: None of the investigated feature selection-classifier combinations provided consistently superior performance under the studied conditions for different sample si
维普期刊数据库
详细信息
万方期刊数据库
同方期刊数据库
springerlink期刊
详细信息
