In the class imbalanced data distribution, both the between-class and within-class imbalance issues are critical factors to decrease the performance. To improve the performance of classifier algorithm on the imbalanced data, a hybrid sampling algorithm based on probability distribution estimation is proposed. The approach re-samples the data of subclass to balance the distribution in each class based on probability distribution estimation. Moreover, it expands the decision region of minority class and removes the redundant information of majority class, so as to solve the imbalance issues from both global and local perspectives simultaneously. Experimental results show that the proposed method improves the classification performance for imbalanced data.