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Abstract: Two strategies for averaging classification tree models are proposed. The first strategy is based on a probability distribution proposed for an equivalence class of classification trees (i.e., those that ignore the value of the cut points but retain tree structure). This distribution is parameterized by a central tree structure representing the true model, and a precision or concentration coefficient representing the variability around the central tree. We propose the maximum likelihood... (Update)
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BibTeX entry: (Update)
@misc{ shannon-averaging,
author = "William Shannon",
title = "Averaging Classification Tree Models Interface '98 Proceedings (In Press)",
url = "citeseer.ist.psu.edu/184125.html" }
Citations (may not include all citations):657 Bagging predictors - Breiman
367 Stacked generalization - Wolpert
109 Stacked regressions (context) - Breiman
63 Wadsworth and Brooks (context) - Breiman, Friedman - 1984
41 Heuristics of instability and stabilization in model selecti.. (context) - Breiman - 1994
30 Bayesian CART Model Search - Chipman, George et al. - 1998
22 Comparing Partitions (context) - Hubert, Arabie - 1985
9 Metric inference for social networks (context) - Banks, Carley - 1994
5 Pattern classification: A unified view of statistical and ne.. (context) - Schurman - 1996
4 An MLE strategy for combining CART Models (context) - Shannon, Banks - 1997
4 A distance metric for classification trees (context) - Shannon, Banks - 1997
3 Combining classification trees using MLE (context) - Shannon, Banks - 1998
2 Locally Bagged Decision Trees (context) - Rao, Potts
2 Metric Models for Random Graphs (context) - Banks, Constantine - 1998
2 World Scientific Publishing Co (context) - Arabie, Hubert et al. - 1996
[Article contains additional citations not shown here]
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