Classification of Large Imbalanced Credit Client Data with Cluster Based SVM

Abstract

Credit client scoring on medium sized data sets can be accomplished by means of Support Vector Machines (SVM), a powerful and robust machine learning method. However, real life credit client data sets are usually huge, containing up to hundred thousands of records, with good credit clients vastly outnumbering the defaulting ones. Such data pose severe computational barriers for SVM and other kernel methods, especially if all pairwise data point similarities are requested. Hence, methods which avoid extensive training on the complete data are in high demand. A possible solution is clustering as preprocessing and classification on the more informative resulting data like cluster centers. Clustering variants which avoid the computation of all pairwise similarities robustly filter useful information from the large imbalanced credit client data set, especially when used in conjunction with a symbolic cluster representation. Subsequently, we construct credit client clusters representing both client classes, which are then used for training a non standard SVM adaptable to our imbalanced class set sizes. We also show that SVM trained on symbolic cluster centers result in classification models, which outperform traditional statistical models as well as SVM trained on all our original data.