Solving imbalanced classification problems with support vector machines

The Support Vector Machine (SVM) is a powerful learning mechanism and promising results have been obtained in the field of medical diagnostics and text-categorization. However, successful applications to business oriented classification problems are still limited. Most real world data sets exhibit vast class imbalances and an accurate identification of the economical relevant minority class is a major challenge within this domain. Based upon an empirical experiment, we evaluate the adequacy of SVMs to identify the respondents of a mailing campaign, massively underrepresented in our data set finding SVM to be capable of handling class imbalances in an internal manner providing robust and competitive results when compared to re-sampling methods which are commonly used to account for class imbalances. Consequently, the overall process of data pre-processing is simplified when applying a SVM classifier leading to less time consuming and more cost-efficient analysis.