2002 BayesianTreedModels

• (Chipman et al., 2002) ⇒ Hugh A. Chipman, Edward I. George, and Robert E. McCulloch. (2002). “Bayesian Treed Models.” In: Machine Learning Journal, 48(1-3). doi:10.1023/A:1013916107446

Subject Headings: Bayesian Tree Modeling Algorithm.

Cited By

• http://scholar.google.com/scholar?q=%222002%22+Bayesian+Treed+Models
• http://dl.acm.org/citation.cfm?id=599615.599691&preflayout=flat#citedby

Quotes

Abstract

When simple parametric models such as linear regression fail to adequately approximate a relationship across an entire set of data, an alternative may be to consider a partition of the data, and then use a separate simple model within each subset of the partition. Such an alternative is provided by a treed model which uses a binary tree to identify such a partition. However, treed models go further than conventional trees (e.g. CART, C4.5) by fitting models rather than a simple mean or proportion within each subset. In this paper, we propose a Bayesian approach for finding and fitting parametric treed models, in particular focusing on Bayesian treed regression. The potential of this approach is illustrated by a cross-validation comparison of predictive performance with neural nets, MARS, and conventional trees on simulated and real data sets.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2002 BayesianTreedModels	Hugh A. Chipman Edward I. George Robert E. McCulloch			Bayesian Treed Models				10.1023/A:1013916107446		2002