Introduction

The combination of classifiers can help reduce misclassification errors substantially. Many studies have proved such ensembling methods can significantly reduce the variance of the prediction model. Several techniques have been proposed to achieve a variance reduction. For example, in many cases, bootstrap aggregating (bagging) classification trees have been shown to have higher accuracy than a single classification tree. Bagging can be applied to tree-based algorithms to enhance the accuracy of the predictions, although it can be used with methods other than tree-based methods as well.