Abstract: Ensemble learning has attracted wide attention in imbalanced category circumstances such as information retrieval,image processing,and biology due to its generalization ability.To improve the performance of classification algorithm on imbalanced data,this paper proposed an ensemble learning algorithm,namely Oversampling Based on Probabi-lity Distribution-Embedding Feature Selection in Boosting (OBPD-EFSBoost).This algorithm mainly includes three steps.Firstly,the original data are oversampled based on probability distribution estimation to construct a balanced dataset.Secondly,when training base classifiers in each round,OBPD-EFSBoost increases the weight of misclassified samples,and considers the effect of noise feature on classification results,thus filtering the redundant noise feature.Finally,the eventual ensemble classifier is obtained through weighted voting on different base classifiers.Experimental results show that the algorithm not only improves the classification accuracy for minority class,but also eliminates the sensitivity of Boosting to noise features,and it has strong robustness.

Key words: Ensemble learning, Feature selection, Imbalanced data classification, Probability distribution