Nowadays, in the insurance industry the use of predictive modeling by means of regression and classification techniques is becoming increasingly important and popular. The success of an insurance company largely depends on the ability to perform such tasks as credibility estimation, determination of insurance premiums, estimation of probability of claim, detecting insurance fraud, managing insurance risk. This paper discusses regression and classification modeling for such types of prediction problems using the method of Adaptive Basis Function Construction
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 V. Cherkassky and F. M. Mulier Learning from Data: Concepts Theory and Methods 2nd ed. Wiley-IEEE Press 2007. http://dx.doi.org/10.1002/9780470140529
 T. Hastie R. Tibshirani and J. Friedman The Elements of Statistical Learning. Springer 2003.
 G. Jekabsons "Ensembling Adaptively Constructed Polynomial Regression Models" International Journal of Intelligent Systems and Technologies (IJIST). vol. 3 no. 2 pp. 56-61 2008.
 G. Jekabsons and J. Lavendels "Polynomial Regression Modelling using Adaptive Construction of Basis Functions" Proceedings of IADIS International Conference Applied Computing 2008 Algarve Portugal pp. 269-276 2008.
 G. Jekabsons "Adaptive Basis Function Construction: An Approach for Adaptive Building of Sparse Polynomial Regression Models" in Machine Learning Y. Zhang Ed. In-Tech 2010 pp. 127-156.
 P. Komarek and A. Moore "Making Logistic Regression A Core Data Mining Tool With TR-IRLS" Proceedings of the 5th International Conference on Data Mining Machine Learning 2005.
 J. O. Rawlings Applied Regression Analysis: A Research Tool 2nd ed. Pacific Grove CA: Wadsworth & Brooks/Cole 1998. http://dx.doi.org/10.1007/b98890
 J. Reunanen "Search Strategies" Feature Extraction: Foundations and Applications I. Guyon S. Gunn M. Nikravesh L. A. Zadeh Eds. Springer pp. 119-137 2006. http://dx.doi.org/10.1007/978-3-540-35488-8_5
 P. Pudil F. J. Ferri J. Novovicova and J. Kittler "Floating search methods for feature selection with nonmonotonic criterion functions" Proceedings of the International Conference on Pattern Recognition vol. 2 Los Alamitos CA: IEEE pp. 279-283 1994.
 P. Pudil J. Novovicova and J. Kittler "Floating Search Methods in Feature Selection" Pattern Recognition Letters vol. 15. pp. 1119-1125 1994. http://dx.doi.org/10.1016/0167-8655(94)90127-9
 C. M. Hurvich and C-L. Tsai "Regression and Time Series Model Selection in Small Samples" Biometrika 76 pp. 297-307 1989. http://dx.doi.org/10.1093/biomet/76.2.297
 A. K. Jain D. P. W. Duin and J. Mao "Statistical pattern recognition: a review" IEEE Transactions on Pattern Analysis and Machine Intelligence vol. 22 no. 1 2000. http://dx.doi.org/10.1109/34.824819
 D. Zongker and A. Jain "Algorithms for feature selection: an evaluation" Pattern Recognition vol. 2 pp. 18-22 1996.
 M. Kudo and J. Sklansky "Comparison of algorithms that select features for pattern classifiers" Pattern Recognition vol. 33 no. 1 pp. 25-41 2000. http://dx.doi.org/10.1016/S0031-3203(99)00041-2
 H. Akaike "A new look at the statistical model identification" IEEE Transactions on Automatic Control AC-19 pp. 716-723 1974. http://dx.doi.org/10.1109/TAC.1974.1100705
 K. Kalnins E. Eglitis G. Jekabsons and R. Rikards "Metamodels for Optimum Design of Laser Welded Sandwich Structures" Scientific Proceedings of International Conference on Welded Structures Design Fabrication and Economy 2008 Miskolc Hungary pp. 119-126 2008.
 K. Kalnins O. Ozolins and G. Jekabsons "Metamodels in Design of GFRP Composite Stiffened Deck Structure" Proceedings of 7th ASMOUK/ ISSMO International Conference on Engineering Design Optimization Association for Structural and Multidisciplinary Optimization in the UK Bath UK 11 p. 2008.
 K. Kalnins G. Jekabsons K. Zudrags and R. Beitlers "Metamodels in optimisation of plywood sandwich panels" Shell Structures: Theory and Applications W. Pietraszkiewicz C. Szymczak Eds. CRC Press pp. 291-294 2009.
 P. van der Putten and M. van Someren Eds. "CoIL Challenge 2000: The Insurance Company Case" Sentient Machine Research Leiden Institute of Advanced Computer Science Amsterdam The Netherlands Tech. Rep. 2000-09 2000.
 I. H. Witten E. Frank Data mining: practical machine learning tools and techniques with Java implementations 2nd ed. SF: Morgan Kaufmann 2005.