Eibe Frank, Geoffrey Holmes, Richard Kirkby, Mark Hall  Home/Search
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Abstract: This paper proposes a method for generating classifiers from large datasets by building a committee of simple base classifiers using a standard boosting algorithm. It permits the processing of large datasets even if the underlying base learning algorithm cannot efficiently do so. (Update)
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BibTeX entry: (Update)
E. Frank, G. Holmes, R. Kirkby, and M. Hall. Racing committees for large datasets. In Discovery Science, 2002. http://citeseer.ist.psu.edu/frank02racing.html More
@misc{ frank02racing,
author = "E. Frank and G. Holmes and R. Kirkby and M. Hall",
title = "Racing committees for large datasets",
text = "E. Frank, G. Holmes, R. Kirkby, and M. Hall. Racing committees for large
datasets. In Discovery Science, 2002.",
year = "2002",
url = "citeseer.ist.psu.edu/frank02racing.html" }
Citations (may not include all citations):696 UCI repository of machine learning databases (context) - Blake, Keogh et al. - 1998
500 Experiments with a new boosting algorithm - Freund, Schapire - 1996
236 Additive logistic regression: a statistical view of boosting - Friedman, Hastie et al. - 2000
62 Pruning adaptive boosting - Margineantu, Dietterich - 1997
49 The UCI KDD archive (context) - Hettich, Bay - 1999
32 Static versus dynamic sampling for data mining - John, Langley - 1996
21 Arcing classi ers (context) - Breiman - 1998
21 A study of two sampling methods for analysing large datasets.. - Srinivasan - 1999
9 Cost complexity-based pruning of ensemble classi ers - Prodromidis, Stolfo - 2001
9 A streaming ensemble algorithm (context) - Street, Kim - 2001
7 The application of AdaBoost for distributed (context) - Fan, Stolfo et al. - 1999
5 Pasting small votes for classi cation in large databases and.. (context) - Breiman - 1999
3 Experimental comparisons of online and batch versions of bag.. - Oza, Russell - 2001
2 Pruning classi ers in a distributed meta-learning system (context) - Prodromidis, Stolfo et al. - 1998
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