On a strategy to develop robust and simple tariffs from motor vehicle insurance data - Andreas Christmann (Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen, 2004)
The goals of this paper are twofold: we describe common features in data sets from motor vehicle insurance companies and we investigate a general strategy which exploits the knowledge of such features. The results of the strategy are a basis to develop insurance tariffs. The strategy is applied to a data set from motor vehicle insurance companies. We use a nonparametric approach based on a combination of kernel logistic regression and support vector regression.
Keywords: 519; Classification; Data Mining; Insurance tariffs; Kernel logistic regression; Machine learning; Regression; Robustness; Simplicity; Support Vector Machine; Support Vector Regression; Kraftfahrtversicherung; Versicherungsprämie; Regression; Theorie; Data Mining