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

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.