Response modeling with support vector machines

Authors:
HyunJung Shin;Sungzoon Cho
Affiliations:
Friedrich Miescher Laboratory, Max Planck Society, Spemannstr. 37, 72076 Tübingen, Germany;Department of Industrial Engineering, College of Engineering, Seoul National University, San 56-1, Shillim-Dong, Kwanak-Gu, 151-744 Seoul, South Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2006

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Abstract

Support Vector Machine (SVM) employs Structural Risk Minimization (SRM) principle to generalize better than conventional machine learning methods employing the traditional Empirical Risk Minimization (ERM) principle. When applying SVM to response modeling in direct marketing, however, one has to deal with the practical difficulties: large training data, class imbalance and scoring from binary SVM output. For the first difficulty, we propose a way to alleviate or solve it through a novel informative sampling. For the latter two difficulties, we provide guidelines within SVM framework so that one can readily use the paper as a quick reference for SVM response modeling: use of different costs for different classes and use of distance to decision boundary, respectively. This paper also provides various evaluation measures for response models in terms of accuracies, lift chart analysis, and computational efficiency.