Bundling classifiers by bagging trees

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• 作者：Hothorn ; Torsten ; Lausen ; Berthold
• 关键词：Bagging ; Ensemble-methods ; Method selection ; Error rate estimation
• 刊名：Computational Statistics and Data Analysis
• 出版年：2005
• 出版时间：June 15, 2005
• 年：2005
• 卷：49
• 期：4
• 页码：1068-1078
• 全文大小：209 K

文摘

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.