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Vote-boosting is a sequential ensemble learning method in which the individual classifiers are built on different weighted versions of the training data. To build a new classifier, the weight of each training instance is determined in terms of the degree of disagreement among the current ensemble predictions for that instance. For low class-label noise levels, especially when simple base learners...
The properties of bootstrap ensembles, such as bagging or random forest, are utilized to detect and handle label noise in classification problems. The first observation is that subsampling is a regularization mechanism that can be used to render bootstrap ensembles more robust to this type of noise. Furthermore, appropriate values of the sampling rate can be estimated using out-of-bag data. A second...
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