关键词: 非平衡数据, 推进算法, 怀卡托智能分析环境（WEKA）系统, F度量

Abstract: The application of data classification in reality usually confronts to a problem named imbalanced data that the amount of one class is larger than another class.At the present time，as one of the solutions to classification of imbalanced data，Boosting has a great prospect because the whole performance of classification can be improved by increasing the minority class’s F-Measure in the form of iteration.This paper will analyze the reason that the performance of imbalanced data is weak，and improve this classical Boosting algorithm by restraining from overfitting and controlling the F-Measure of minority class，and propose an improved algorithm named RIFBoost，and then compare this new algorithm with some traditional classified algorithms on WEKA system.The experiment result indicates that RIFBoost can increase the F-Measure of minority class while maintaining the whole of classification’s precision.

Key words: imbalanced data, boosting algorithm, Waikato Environment for Knowledge Analysis（WEKA） system, F-measure