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 may be a combined cluster and classification approach. Computationally efficient clustering can compress information from the large data set in a robust way, especially in conjunction with a symbolic cluster representation. Credit client data clustered with this procedure will be used in order to estimate classification models.
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Stecking, R., Schebesch, K.B. (2013). Symbolic Cluster Representations for SVM in Credit Client Classification Tasks. In: Giudici, P., Ingrassia, S., Vichi, M. (eds) Statistical Models for Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00032-9_40
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DOI: https://doi.org/10.1007/978-3-319-00032-9_40
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