A Novel Noise-Resistant Boosting Algorithm for Class-Skewed Data

Boosting methods have been successfully applied in a wide variety of machine learning applications. In the context of data quality issues, a number of variants of the standard boosting method have been proposed and evaluated. To address the problem of mislabeled examples, ORBoost was developed to prevent over fitting to noisy examples. Our research group has recently proposed RUSBoost as an enhancement to the AdaBoost algorithm for dealing with skewed class distributions. This work proposes a modification to the RUSBoost algorithm, incorporating the noise-handling ability of ORBoost, to improve its handling of noisy data. The new method is compared with both ORBoost and RUSBoost in an extensive set of experiments using five real-world datasets with various levels of simulated noise.