Abstract
Data preprocessing and a careful selection of the training and classification method are key steps for building a predictive or classification model with high performance. Here, we present the winner approaches submitted to the 2005 GfKl Data Mining Competition. The task to be solved for the competition was the prediction of a possible liquidity crisis of a company. The binary classification was to be based on a set of 26 variables describing attributes of the companies with unknown semantics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
VOGEL, D., and WANG, M. (2004): 1-Dimensional Splines as Building Blocks Improving Accuracy of Risk Outcomes Models. ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 841–846.
BERGER, A.L., DELLA PIETRA, S., and DELLA PIETRA, V.J. (1996): A Maximum Entropy Approach to Natural Language Processing. Computational Linguistics, 22(1):39–71.
BISHOP, C.M. (1996): Neural Networks for Pattern Recognition. Clarendon Press.
KEYSERS, D., OCH, F.J., and NEY, H. (2002): Efficient Maximum Entropy Training for Statistical Object Recognition. Informatiktage 2002 der Gesellschaft Informatik, 342–345. Bad Schussenried, Germany.
LANDWEHR, N., HALL, M., and FRANK, E. (2003): Logistic Model Trees. 14th Int. Conference on Machine Learning, 241–252. Springer-Verlag, Germany.
NABNEY, I.T. (2001): Netlab. Algorithms for Pattern Recognition. Advances Pattern Recognition. Springer-Verlag Telos.
WITTEN, I.H., and FRANK, E. (1999): Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, CA, USA.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer Berlin · Heidelberg
About this paper
Cite this paper
Strackeljan, J. et al. (2006). GfKl Data Mining Competition 2005: Predicting Liquidity Crises of Companies. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (eds) From Data and Information Analysis to Knowledge Engineering. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31314-1_92
Download citation
DOI: https://doi.org/10.1007/3-540-31314-1_92
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31313-7
Online ISBN: 978-3-540-31314-4
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)