Experiments with a New Boosting Algorithm

Experiments with a New Boosting Algorithm (1996)

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by Yoav Freund , Robert E. Schapire

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2208 - 20 self

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@MISC{Freund96experimentswith,
    author = {Yoav Freund and Robert E. Schapire},
    title = {Experiments with a New Boosting Algorithm},
    year = {1996}
}

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Abstract

In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced the related notion of a “pseudo-loss ” which is a method for forcing a learning algorithm of multi-label conceptsto concentrate on the labels that are hardest to discriminate. In this paper, we describe experiments we carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems. We performed two sets of experiments. The first set compared boosting to Breiman’s “bagging ” method when used to aggregate various classifiers (including decision trees and single attribute-value tests). We compared the performance of the two methods on a collection of machine-learning benchmarks. In the second set of experiments, we studied in more detail the performance of boosting using a nearest-neighbor classifier on an OCR problem.

Keyphrases

new boosting algorithm learning algorithm machine-learning benchmark related notion decision tree multi-label conceptsto concentrate nearest-neighbor classifier ocr problem second set various classifier real learning problem first set single attribute-value test