Designing Classifier Ensembles with Constrained Performance Requirements

Classification requirements for real-world classification problems are often constrained by a given true positive or false positive rate to ensure that the classification error for the most important class is within a desired limit. For a sufficiently high true positive rate, this may result in the set-point being located somewhere in the flat portion of the ROC curve where the associated false positive rate is high. Any further classifier design will then attempt to reduce the false positive rate while maintaining the desired true positive rate. We call this type of performance requirements for classifier design the constrained performance requirement. This type of performance requirements is different from the accuracy maximization requirement and thus requires different strategies for classifier design. This paper is concerned with designing classifier ensembles under such constrained performance requirements. Classifier ensembles are one of the most significant advances in pattern recognition/classification in recent years and have been actively studied by many researchers. However, not much attention has been given to designing ensembles to satisfy constrained performance requirements. This paper attempts to identify and address some of design related issues associated with the constrained performance requirement. Specifically, we present a design strategy for designing neural network ensembles to satisfy constrained performance requirements, which is illustrated by designing a real-world classification problem. The results are compared to those from conventional design method.