Focusing on non-respondents: Response modeling with novelty detectors

This paper proposes to use novelty detection approaches to alleviate the class imbalance in response modeling. Two novelty detectors, one-class support vector machine (1-SVM) and learning vector quantization for novelty detection (LVQ-ND), are compared with binary classifiers for a catalogue mailing task with DMEF4 dataset. The novelty detectors are more accurate and more profitable when the response rate is low. When the response rate is relatively high, however, a support vector machine model with modified misclassification costs performs the best. In addition, the novelty detectors turn in higher profits with a low mailing cost, while the SVM model is the most profitable with a high mailing cost.