Biomedical Spectral Classification Using Stochastic Feature Selection and Fuzzy Aggregation

Classifying magnetic resonance spectra is often difficult due to the curse of dimensionality; a high-dimensional feature space couple with a small sample size. We present an aggregation strategy that combines predicted disease states from multiple classifiers with the anticipated outcome that the aggregated predictions are superior to any individual classifier prediction. Multiple classifiers are presented with different, randomly selected, subsets of spectral features. The fuzzy integration results are compared against the best individual classifier operating on a spectral feature subset.