Using Model Trees for Classification
Model trees, which are a type of decision tree with linear regression functions at the leaves, form the basis of a recent successful technique for predicting continuous numeric values. They can be applied to classification problems by employing a standard method of transforming a classification problem into a problem of function approximation. Surprisingly, using this simple transformation the model tree inducer M5′, based on Quinlan's M5, generates more accurate classifiers than the state-of-the-art decision tree learner C5.0, particularly when most of the attributes are numeric.
- Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and regression trees. Belmont CA: Wadsworth.
- Devroye, L., Gyoerfi, L. & Lugosi, G. (1996). A probabilistic theory of pattern recognition. New York: Springer-Verlag.
- Dietterich, T.G. & Bakiri G. (1995). “Solving multiclass learning problems via error-correcting output codes,” Journal of AI research, 2, 263-286.
- Holte, R.C. (1993). “Very simple classification rules perform well on most commonly used datasets,” Machine Learning, 11, 63-91. CrossRef
- Merz, C.J. & Murphy, P.M. (1996). UCI Repository of machine learning data-bases[http://www. ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science.
- Quinlan, J.R. (1992). “Learning with continuous classes,” Proceedings Australian Joint Conference on Artificial Intelligence(pp. 343-348). World Scientific, Singapore.
- Quinlan, J.R. (1993). C4.5: Programs for machine learning. Morgan Kaufmann.
- Smyth, P., Gray, A. & Fayyad, U. (1995). “Retrofitting Decision tree classifiers using Kernel Density Estimation,” Proceedings International Conference on Machine Learning(pp. 506-514). San Francisco, CA: Morgan Kaufmann.
- Torgo, L. (1997). “Kernel Regression Trees,” Proceedings of the poster papers of the European Conference on Machine Learning. University of Economics, Faculty of Informatics and Statistics, Prague.
- Wang, Y. & Witten, I.H. (1997). “Induction of model trees for predicting continuous classes,” Proceedings of the poster papers of the European Conference on Machine Learning, University of Economics, Faculty of Informatics and Statistics, Prague.
- Using Model Trees for Classification
Volume 32, Issue 1 , pp 63-76
- Cover Date
- Print ISSN
- Online ISSN
- Kluwer Academic Publishers
- Additional Links
- Model trees
- classification algorithms
- decision trees
- Industry Sectors