关键词: 不平衡数据集, 二分类, 曲线下面积（AUC）, 度量指标优化, Boosting算法

Abstract: In order to improve the classification performance of imbalanced data, optimization boosting of imbalanced data based on metrics is proposed. Instead of error rate, the algorithm uses metric of imbalanced data classification into Boosting algorithm, the optimization parameters select recall rate of positive and negative with weigh factor, F-measure, G-means. The Alpha value is calculated by the different metrics, combination of weak learners with weighted values is obtained, Boosting algorithm is optimized by above parameters. In the comparison experiments with Boosting algorithm, performance Metrics of AUC values, F-measure and G-mean increased, misclassification rate decreases in some data set, the experiments results show that the algorithm can effectively improve classification performance of imbalanced data.

Key words: imbalanced data sets, binary classification, Area Under roc Curve（AUC）, metrics optimization, Boosting algorithm