期刊
COMPUTATIONAL STATISTICS & DATA ANALYSIS
卷 49, 期 4, 页码 1068-1078出版社
ELSEVIER
DOI: 10.1016/j.csda.2004.06.019
关键词
bagging; ensemble-methods; method selection; error rate estimation
The quest of selecting the best classifier for a discriminant analysis problem is often rather difficult. A combination of different types of classifiers promises to lead to improved predictive models compared to selecting one of the competitors. An additional learning sample, for example the out-of-bag sample, is used for the training of arbitrary classifiers. Classification trees are employed to bundle their predictions for the bootstrap sample. Consequently, a combined classifier is developed. Benchmark experiments show that the combined classifier is superior to any of the single classifiers in many applications. (c) 2004 Elsevier B.V. All rights reserved.
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