Support Vector Machine Classification of Uncertain and Imbalanced data using Robust Optimization

In this paper, we have developed a robust Support Vector Machines (SVM) scheme of classifying imbalanced and noisy data using the principles of Robust Optimization. Uncertainty is prevalent in almost all datasets and has not been addressed efficiently by most data mining techniques, as these are based on deterministic mathematical tools. Imbalanced datasets exist while performing analysis of rare events, and for such datasets elements in the minority class become critical. Our method tries to address both issues lacking in traditional SVM classifications. At present, we provide solutions for linear classification of data having bounded uncertainties. This can be extended to non-linear classification schemes for any types of uncertainties that are convex. Our results in predicting the importance of the minority class are better than the traditional SVM soft-margin classification. Preliminary computational results are presented. Key-Words: Support Vector Machines, Robust Classification, Imbalance, Uncertainty, Noise