Additive Logistic Regression: a Statistical View of Boosting

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Additive Logistic Regression: a Statistical View of Boosting (1998)

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by Jerome Friedman , Trevor Hastie , Robert Tibshirani

Venue:	Annals of Statistics
Citations:	1748 - 25 self

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@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 1996, Schapire & Singer 1998) is one of the most important recent developments in classification methodology. The performance of many classification algorithms can often be dramatically improved by sequentially applying them to reweighted versions of the input data, and taking a weighted majority vote of the sequence of classifiers thereby produced. 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-class generalizations of boosting in most...

Keyphrases

additive logistic regression statistical view input data classification methodology statistical principle maximum likelihood many classification algorithm identical result weighted majority vote schapire singer important recent development mysterious phenomenon multinomial likelihood multi-class generalization direct approximation two-class problem maximum bernoulli likelihood logistic scale direct multi-class generalization exhibit performance additive modeling freund schapire

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