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The alternating decision tree learning algorithm (1999) [65 citations — 6 self]

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@INPROCEEDINGS{Freund99thealternating,
    author = {Yoav Freund},
    title = {The alternating decision tree learning algorithm},
    booktitle = {In Machine Learning: Proceedings of the Sixteenth International Conference},
    year = {1999},
    pages = {124--133},
    publisher = {Morgan Kaufmann}
}

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Abstract:

1 INTRODUCTION The AdaBoost algorithm [7, 16] has recently proved to be an important component in practical learning algorithms. Two of the most successful combinations have been boosting decision trees and boosting stumps [6, 1, 13, 8]. Stumps are the simplest special case of decision trees which consist of a single decision node and two prediction leaves. Boosting decision trees learning algorithms, such as CART [2] and C4.5 [14], yields very good classifiers.

Citations

2578	Classification and Regression Trees – Breiman, Friedman, et al. - 1984
1210	A decision-theoretic generalization of on-line learning and an application to boosting – Freund, Schapire - 1997
1048	Experiments with a new boosting algorithm – Freund, Schapire - 1996
598	Additive logistic regression: a statistical view of boosting – Friedman, Hastie, et al. - 2000
145	Classi cation and Regression Trees – Breiman, Friedman, et al. - 1984
96	Learning Classification Trees – Buntine - 1992
74	arcing classifiers – Bias - 1996
62	Extracting comprehensible models from trained neural networks. Doctoral dissertation – Craven - 1996
47	Knowledge acquisition from examples via multiple models – Domingos - 1997
13	Learning classi cation trees – Buntine - 1992