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Additive Logistic Regression: a Statistical View of Boosting (1998)
| Venue: | Annals of Statistics |
| Citations: | 1706 - 25 self |
BibTeX
@ARTICLE{Friedman98additivelogistic,
author = {Jerome Friedman and Trevor Hastie and Robert Tibshirani},
title = {Additive Logistic Regression: a Statistical View of Boosting},
journal = {Annals of Statistics},
year = {1998},
volume = {28},
pages = {2000}
}
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Abstract
Boosting (Freund & Schapire 1995) is one of the most important recent developments in classification methodology. Boosting works by sequentially applying a classification algorithm to reweighted versions of the training data, and then taking a weighted majority vote of the sequence of classifiers thus produced. For many classification algorithms, this simple strategy results in dramatic improvements in performance. We show that this seemingly mysterious phenomenon can be understood in terms of well known statistical principles, namely additive modeling and maximum likelihood. For the two-class problem, boosting can be viewed as an approximation to additive modeling on the logistic scale using maximum Bernoulli likelihood as a criterion. We develop more direct approximations and show that they exhibit nearly identical results to boosting. Direct multi-class generalizations based on multinomial likelihood are derived that exhibit performance comparable to other recently proposed multi-cl...
Keyphrases
additive logistic regression statistical view classification methodology statistical principle simple strategy result many classification algorithm weighted majority vote identical result reweighted version important recent development additive modeling multinomial likelihood training data dramatic improvement maximum likelihood direct approximation two-class problem maximum bernoulli likelihood logistic scale classification algorithm direct multi-class generalization exhibit performance mysterious phenomenon freund schapire
