Imbalanced Data Classification Based on a Novel Modified AdaBoost algorithm

Traditional Gentle AdaBoost Algorithm always use over-sampling way to accomplish the implementation of minority samples in the process of dealing with the classified issues of unbalanced data set for the purpose of achieving the balance of data set.But this method will incorporate the singular sample which is hard to classified,and lead to the unsatisfied classification performance of the classifier.Therefore,this paper proposes an improved Gentle AdaBoost algorithm specified for the classified issues of unbalanced data set.Firstly,considering the feature that misclassification samples is assigned with a large weight when the classifier is based on Gentle AdaBoost algorithm in training,we can decide the weight threshold for the copy samples,and then,copy a number of minority samples in the threshold range,and use the aforesaid data set to train the classifier and obtain related weak classifier.Repeat the former procedures to balance the data set so that the strong classifier can be also obtained.The whole process has the capability of avoiding the issue of incorporating singular samples in the process of data over-sampling.The experiment demonstrates validity of our algorithm.