Classification algorithms used to support the decisions of human analysts are often used in settings in which zero-one loss is not the appropriate indication of performance. The zero-one loss corresponds to the operating point with equal costs for false alarms and missed detections, and no option for the classifier to leave uncertain test samples unlabeled. A generalization bound for ensemble classification at the standard operating point has been developed based on two interpretable properties of the ensemble: strength and correlation, using the Chebyshev inequality. Such generalization bounds for other operating points have not been developed previously and are developed in this paper. Significantly, the bounds are empirically shown to have much practical utility in determining optimal parameters for classification with a reject option, classification for ultralow probability of false alarm, and classification for ultralow probability of missed detection. Counter to the usual guideline of large strength and small correlation in the ensemble, different guidelines are recommended by the derived bounds in the ultralow false alarm and missed detection probability regimes.

The generalization error, or probability of misclassification, of ensemble classifiers has been shown to be bounded above by a function of the mean correlation between the constituent (i.e., base) classifiers and their average strength. This bound suggests that increasing the strength and/or decreasing the correlation of an ensemble's base classifiers may yield improved performance under the assumption of equal error costs. However, this and other existing bounds do not directly address application spaces in which error costs are inherently unequal. For applications involving binary classification, Receiver Operating Characteristic (ROC) curves, performance curves that explicitly trade off false alarms and missed detections, are often utilized to support decision making. To address performance optimization in this context, we have developed a lower bound for the entire ROC curve that can be expressed in terms of the class-specific strength and correlation of the base classifiers.

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.