Support Vector Machines (SVM) are very successful kernel based classification methods with a broad range of applications including credit scoring and rating. SVM can use data sets with many variables even when the number of cases is small. However, we are often constrained to reduce the input space owing to changing data availability, cost and speed of computation. We first evaluate variable subsets in the context of credit scoring. Then we apply previous results of using SVM with different kernel functions to a specific subset of credit client variables. Finally, rating of the credit data pool is presented.
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