Randomized feature selection using Scopira

Feature selection is a useful preprocessing strategy when dealing with the classification and interpretation of high-dimensional biomedical data coupled with small sample sizes. A classification technique, exploiting parallelization efficiencies, is presented where sets of linear discriminant functions are designed using randomly selected feature subsets with varying cardinality. This technique, tested with biomedical data acquired from several sources, had fewer classification errors than a conventional linear discriminant analysis strategy. The algorithm development framework used to implement the technique is also discussed.