The phenomenon of data imbalance is very common in real life. In order to improve the overall classification accuracy， classifiers often misclassify minority class at the cost. But in real life， the consequences of misclassifying minority class may be very serious. Considering that the traditional resampling algorithm ignores the relationship between the spatial distribution of data and the sample features of minority class， a new sampling algorithm SABRF （Sampling Algorithm Based on Relationship between Features） was proposed to generate a new sample set. The key distinguishing features of imbalanced dataset were preserved through Pareto-based multi-objective feature selection， and the relationships among key features of minority class samples were captured through XGBoost （eXtreme Gradient Boosting） regression model. In addition， considering the quality of newly generated samples， a new sample selection strategy was proposed to retain better samples. Experiments were conducted on six publicly available UCI datasets and one real post-orthopedic thrombus dataset. Experimental results show that the proposed algorithm has good performance on Area Under receiver operating characteristic Curve （AUC）， F1 score （F1_score） and Geometric Mean （G_mean）. In addition， when using the new samples selected by the sample selection strategy based on multi-index evaluation for classification， the classification result of imbalanced data is also the best， which verifies the effectiveness of the sample selection strategy.