Abstract
This paper provides a maximum likelihood estimation strategy to identify a tree-based model which, being a function of a set of observed optimally pruned trees, represents the final classification model. The strategy is based on a probability distribution and it uses a metric based on structural differences among trees. An example on a real dataset is also presented to show how the procedure works.
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
Banks, D. and Costantine, G. (1998). Metric models for random graphs, Journal of Classification, 15, pp. 199–223.
Breiman L., Friedman J. H., Olshen R. A. and Stone C. J. (1984). Classification and Regression Trees, Wadsworth, Belmont CA.
Cappelli, C. and Siciliano, R. (1998). Strategies for choosing the best decision trees. In: Analyses Multidimensionelle des Donnees,IV Congres International NGUS’97, pp. 101–111, Fernandez, K. and Morineau, A. eds, CISIA Ceresta, St. Mande, France.
Cappelli, C., Mola, F. and Siciliano, R. (1998). An alternative pruning method based on the impurity-complexity measure. In: Proceedings in Computational Statistics, pp. 221–226, Payne, R. and Green, P. eds, Heidelberg: Physica-Verlag.
Malerba, D., Esposito, F. and Semeraro, G. (1995). A further comparison of simplification methods for decision tree pruning. In: Learning from Data: Artificial Intelligence and Statistics, Lecture Notes in Statistics, Fisher, D. and Lanz, H. eds, Berlin: Springer-Verlag.
Mangasarian, O.L. et al. (1995). Breast cancer diagnosis and prognosis via linear programming. Operations Research, 43 (4), pp. 570–577.
Mingers, J. (1989), An empirical comparison of pruning methods for decision tree induction. Machine Learning, 4, pp. 227–243.
Niblett, T., Bratko, I. (1986), Learning decision rules in noisy domains. In Research and Development in Expert Systems, Proceedings of Expert Systems-86,3, Bramer, M.A. Ed., Cambridge: Cambridge University Press.
Quinlan, J.R. (1987), Simplifying decision trees. International Journal of Man-Machine Studies, 27, pp. 221–234.
Shannon, W.D. and Banks, D. (1999), Combining classification trees using MLE. Statistics in Medicine, 18, pp. 727–740.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cappelli, C., Shannon, W.D. (2000). An MLE strategy for combining optimally pruned decision trees. In: Bethlehem, J.G., van der Heijden, P.G.M. (eds) COMPSTAT. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-57678-2_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-57678-2_26
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1326-5
Online ISBN: 978-3-642-57678-2
eBook Packages: Springer Book Archive