Building ultra-low false alarm rate Support Vector Classifier ensembles using Random Subspaces

This paper presents the cost-sensitive random subspace support vector classifier (CS-RS-SVC), a new learning algorithm that combines random subspace sampling and bagging with cost-sensitive support vector classifiers to more effectively address detection applications burdened by unequal misclassification requirements. When compared to its conventional, non-cost-sensitive counterpart on a two-class signal detection application, random subspace sampling is shown to very effectively leverage the additional flexibility offered by the cost-sensitive support vector classifier, yielding a more than four-fold increase in the detection rate at a false alarm rate (FAR) of zero. Moreover, the CS-RS-SVC is shown to be fairly robust to constraints on the feature subspace dimensionality, enabling reductions in computation time of up to 82% with minimal performance degradation.