Class-specific error bounds for ensemble classifiers

Authors:
Ryan J. Prenger;Tracy D. Lemmond;Kush R. Varshney;Barry Y. Chen;William G. Hanley
Affiliations:
Lawrence Livermore National Laboratory, Livermore, CA, USA;Lawrence Livermore National Laboratory, Livermore, CA, USA;IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA;Lawrence Livermore National Laboratory, Livermore, CA, USA;Lawrence Livermore National Laboratory, Livermore, CA, USA
Venue:
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2010

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Abstract

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. We present empirical analyses demonstrating the efficacy of these bounds in predicting relative classifier performance. In addition, we specify performance regions of the ROC curve that are naturally delineated by the class-specific strengths of the base classifiers and show that each of these regions can be associated with a unique set of guidelines for performance optimization of binary classifiers within unequal error cost regimes.