An improvment of weight scheme on adaBoost in the presence of noisy data

Abstract

The first strand of this research is concerned with the classification noise issue. Classification noise, (worry labeling), is a further consequence of the difficulties in accurately labeling the real training data. For efficient reduction of the negative influence produced by noisy samples, we propose a new weight scheme with a nonlinear model with the local proximity assumption for the Boosting algorithm. The effectiveness of our method has been evaluated by using a set of University of California Irvine Machine Learning Repository (UCI) [1] benchmarks. We report promising results.