基于隐变量后验生成对抗网络的不平衡学习

何新林, 戚宗锋, 李建勋

Unbalanced Learning of Generative Adversarial Network Based on Latent Posterior

HE Xinlin, QI Zongfeng, LI Jianxun

表3 基于数据过采样的迁移学习分类器指标

Tab.3 Metrics of transfer learning classifier based on data oversampling

指标 数据集 ROS SMOTE Border MWMOTE ADASYN TrAdaboost LGOS Recall phoneme 0.8266 0.8333 0.8466 0.8400 0.8500 0.8433 0.8633 satimage 0.9512 0.9390 0.9390 0.9512 0.9634 0.9512 0.9756 pen 1.0000 1.0000 0.9907 0.9953 1.0000 0.9953 1.0000 wine 0.5166 0.6166 0.6277 0.6388 0.5944 0.6944 0.7722 letter 0.9152 0.9186 0.9152 0.9220 0.9322 0.9220 0.9491 avila 0.9862 0.9862 0.9862 0.9954 0.9862 0.9954 1.0000 F-measure phoneme 0.8378 0.8361 0.8396 0.84 0.8388 0.8281 0.8477 satimage 0.9512 0.9565 0.9506 0.9512 0.9634 0.9512 0.9696 pen 0.9953 0.9976 0.9930 0.9976 0.9953 0.9976 1.0000 wine 0.5942 0.6646 0.6420 0.6301 0.6114 0.5868 0.6698 letter 0.9540 0.9559 0.9523 0.9560 0.9649 0.9560 0.9705 avila 0.9930 0.9907 0.9930 0.9954 0.9907 0.9954 1.0000 G-mean phoneme 0.8832 0.8843 0.8895 0.8879 0.8901 0.8835 0.8976 satimage 0.9728 0.9678 0.9672 0.9728 0.9797 0.9728 0.9858 pen 0.9994 0.9997 0.9951 0.9976 0.9994 0.9976 1.0000 wine 0.7058 0.7700 0.7711 0.7739 0.7490 0.7870 0.8402 letter 0.9565 0.9583 0.9564 0.9599 0.9655 0.9599 0.9739 avila 0.9930 0.9928 0.9930 0.9974 0.9928 0.9974 1.0000 AUC phoneme 0.8851 0.8859 0.8906 0.8892 0.8910 0.8845 0.8983 satimage 0.9731 0.9682 0.9676 0.9731 0.9798 0.9731 0.9859 pen 0.9994 0.9997 0.9951 0.9976 0.9994 0.9976 1.0000 wine 0.7404 0.7891 0.7875 0.7881 0.7690 0.7932 0.8432 letter 0.9574 0.9591 0.9573 0.9607 0.9661 0.9607 0.9743 avila 0.9931 0.9928 0.9931 0.9974 0.9928 0.9974 1.0000