Boosting and instability for regression trees

Authors:
Servane Gey;Jean-Michel Poggi
Affiliations:
Laboratoire MAP5, Université Paris V, 45, rue des Saints Peres, 75006 Paris Cedex 06, France;Laboratoire de mathématiques, U.M.R. 8628, Université Paris XI, 91405 Orsay, France
Venue:
Computational Statistics & Data Analysis
Year:
2006

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Abstract

The AdaBoost like algorithm for boosting CART regression trees is considered. The boosting predictors sequence is analysed on various data sets and the behaviour of the algorithm is investigated. An instability index of a given estimation method with respect to some training sample is defined. Based on the bagging algorithm, this instability index is then extended to quantify the additional instability provided by the boosting process with respect to the bagging one. Finally, the ability of boosting to track outliers and to concentrate on hard observations is used to explore a non-standard regression context.