Multivariate feature selection and hierarchical classification for infrared spectroscopy: serum-based detection of bovine spongiform encephalopathy

Abstract

A hierarchical scheme has been developed for detection of bovine spongiform encephalopathy (BSE) in serum on the basis of its infrared spectral signature. In the first stage, binary subsets between samples originating from diseased and non-diseased cattle are defined along known covariates within the data set. Random forests are then used to select spectral channels on each subset, on the basis of a multivariate measure of variable importance, the Gini importance. The selected features are then used to establish binary discriminations within each subset by means of ridge regression. In the second stage of the hierarchical procedure the predictions from all linear classifiers are used as input to another random forest that provides the final classification. When applied to an independent, blinded validation set of 160 further spectra (84 BSE-positives, 76 BSE-negatives), the hierarchical classifier achieves a sensitivity of 92% and a specificity of 95%. Compared with results from an earlier study based on the same data, the hierarchical scheme performs better than linear discriminant analysis with features selected by genetic optimization and robust linear discriminant analysis, and performs as well as a neural network and a support vector machine.