Tree-based Models in Statistics: Three Decades of Research
The interest in tree-structured methods has been growing rapidly in statistics. In fact, all commercial statistical packages and Data Mining tools have been equipped with tree building modules. The research in this field has its roots in early 70s when early papers on recursive partitioning of the feature space (and its result which has the form of a tree) were published in statistical journals. They began intensive research in nonparametric statistical methods for classification, regression, survival analysis etc. The aim of this paper is to summarize achievements of this research and point out some still open problems.
KeywordsFrontal Lobe Multivariate Adaptive Regression Spline Recursive Partitioning Misclassification Error Data Mining Tool
Unable to display preview. Download preview PDF.
- BREIMAN, L. (1999): Using Adaptive Bagging to Debias Regressions. Technical Report, 547, Statistics Department, University of California, Berkeley.Google Scholar
- CAMPBELL, N.A. and MAHON, R.J. (1974): A Multivariate Study of Variation in Two Species of Rock Crab of Genus Leptograpsus. Australian Journal of Zoology, 22, 417–425.Google Scholar
- CARTER, C. and CATLETT, J. (1987): Assessing Credit Card Applications Using Machine Learning. IEEE Expert, Fall Issue, 71–79.Google Scholar
- FRIEDMAN, J. H. (1999): Stochastic Gradient Boosting. Technical Report, Stanford University, Stanford.Google Scholar
- GATNAR, E. (2001): Nonparametric Method for Discrimination and Regression. PWN Scientific Publishers, Warsaw (in Polish).Google Scholar
- GORDON, L. and OLSHEN, R.A. (1985), Tree-structured survival analysis. Cancer Treatment Reports, 69, 1065–1069.Google Scholar
- HASTIE, T. and PREGIBON, D. (1991) Shrinking Trees. Technical Report, AT T Bell Laboratories, Murray Hill, NJ.Google Scholar
- HUNT, E.B., MARIN, J., STONE, P.J. (1966): Experiments in Induction. Academic Press, New York.Google Scholar
- MINGERS, J. (1987): Expert Systems: Rule Induction with Statistical Data. Journal of The Operational Research Society, 38, 39–47.Google Scholar
- PERINEL, E. and LECHEVALLIER, Y. (2000): Symbolic discrimination rule. In: H.H. Bock and E. Diday (Eds.): Analysis of Symbolic Data. Exploratory Methods for Extracting Statistical Information From Complex Data. Springer, Heidelberg.Google Scholar
- QUINLAN, J.R. (1986): Induction of Decision Trees. Machine Learning, 1, 81–106.Google Scholar
- QUINLAN, J.R. (1993): C4. 5: Programs for Machine Learning. Morgan Kaufmann, San Mateo.Google Scholar