Abstract
Due to the high number of insolvencies in the credit business, automatic procedures for testing the credit-worthiness of enterprises become increasingly important. For this task we use classification trees with soft splits which assign the observations near the split boundary to both branches. Tree models involve an extra complication as the number of parameters varies as the tree grows and shrinks. Hence we adapt the reversible jump Markov Chain Monte Carlo procedure to this model which produces an ensemble of trees representing the posterior distribution. For a real-world credit-scoring application our algorithm yields lower classification errors than bootstrapped versions of regression trees (CART), neural networks, and adaptive splines (MARS). The predictive distribution allows to assess the certainty of credit decisions for new cases and guides the collection of additional information.
We thank Prof. Dr. Jörg Baetge, University of Münster for granting access to the dataset.
Chapter PDF
References
J.O. Berger, Stat. Dec. Theory, Foundations, Concepts and Methods. Springer, NY 1980.
L. Breiman, J.H. Friedman, R. Olshen, and C.J. Stone. Classif. and Regr. Trees. Wadsworth Int. Group, Belmont, CA, 1984.
W. Buntine. Learning classification trees. Statistics and Computing, 2:63–73, 1992.
C. Carter and J. Catlett. Assessing credit card appl. using machine learning. IEEE Expert, 2(3):71–79, 1987.
H. Chipman, E. George, and R. McCulloch. Bayesian CART. TR, Dept. of Stat., Univ. of Texas, Austin, 1995.
J. H. Friedman Multivariate adaptive regression splines.Ann. of Stat., 19(1):1–67, 1991.
J.H. Friedman. Local learning based on recursive covering. TR, Stanford Uni, August 1996.
S. Geman, E. Bienenstock, and R. Doursat. Neural networks and the bias/variance dilemma. Neural Computation 4, p.58, 1992.
P. J. Green. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. TR, Bristol Univ., 1995.
R.A. Jacobs, M.I. Jordan, S.J. Nowlan, and G.E. Hinton. Adaptive mixtures of local experts. Neural Computation, 3:79–87, 1991.
Gerhard Paaß. Assessing and improving neural network predictions by the bootstrap algorithm. In S. Hanson, J. Cowan, and C. Giles, editors, NIPS-5, pages 196–203. Morgan Kaufman, San Mateo, CA, San Mateo, CA., 1993.
J.R. Quinlan. C4.5: Prog. f. Machine Learning. Morgan Kaufmann, San Mateo, CA, 1993.
B.D. Ripley. Pattern Recog. and Neural Networks. Cambridge Univ. Press, 1996.
Waterhouse S.R. Classification and Regression using Mixtures of Experts. PhD thesis, Cambridge Univ. Engineering Dept., October 1997.
L. Tierney. Markov chains for expl. post. distr. TR 560, School of Stat., UMinnesota, 1994.
J. Wallrafen. Kreditwürdigkeitsprüfung von Unternehmen mit neuronalen Klassifikationsverfahren. Master’s thesis, University of Erlangen-Nürnberg, 1995.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kindermann, J., Paass, G. (1998). Model switching for bayesian classification trees with soft splits. In: Żytkow, J.M., Quafafou, M. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 1998. Lecture Notes in Computer Science, vol 1510. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0094815
Download citation
DOI: https://doi.org/10.1007/BFb0094815
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65068-3
Online ISBN: 978-3-540-49687-8
eBook Packages: Springer Book Archive