A robust boosting by using an adaptive weight scheme

In the real world, it is extremely difficult to avoid errors; for instance, a doctor may misdiagnose patients. In other words, databases are never free from data entry or other related errors, and many kinds of mistakes are unavoidable in real world data sets. In existing approaches to pattern recognition, handling noisy data in the learning process always produces better generalization performance than if the noise were ignored. In this article, a novel and adaptive weighting mechanism for noise learning tasks is proposed, especially for boosting learning approaches, preventing the algorithm from concentrating on unreasonably noisy learning samples. Several experiments on UC Irvine Machine Learning Repository and a facial expression data set demonstrate the effectiveness of our method.