文章基本信息

标题：ipred: Improved Predictors
本地全文：下载
作者：Andrea Peters ; Torsten Hothorn ; Berthold Lausen 等
期刊名称：R News
印刷版ISSN：1609-3631
出版年度：2002
卷号：2
期号：2
页码：33
出版社：The R Foundation for Statistical Computing
摘要：In classification problems, there are several attempts to create rules which assign future observations to certain classes. Common methods are for example linear discriminant analysis or classification trees. Recent developments lead to substantial reduction of misclassification error in many applications. Bootstrap aggregation (“bagging”, Breiman, 1996a) combines classifiers trained on bootstrap samples of the original data. Another approach is indirect classification, which incorporates a priori knowledge into a classification rule (Hand et al., 2001). Since the misclassification error is a criterion to assess the classification techniques, its estimation is of main importance. A nearly unbiased but highly variable estimator can be calculated by cross validation. Efron and Tibshirani (1997) discuss bootstrap estimates of misclassification error. As a by-product of bagging, Breiman (1996b) proposes the out-of-bag estimator.