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2 基于数据过采样的决策树分类器指标

Tab.2 Metrics of decision tree classifier based on data oversampling

指标 数据集 原始数据 ROS SMOTE Border MWMOTE ADASYN LGOS Recall phoneme 0.7566 0.7396 0.7953 0.7976 0.8023 0.8046 0.8433 satimage 0.9146 0.9365 0.9414 0.9268 0.9512 0.9524 0.9634 pen 0.9583 0.9819 0.9814 0.9814 0.9856 0.9625 0.9861 wine 0.6000 0.5627 0.6511 0.6188 0.6533 0.6583 0.6944 letter 0.9016 0.8759 0.8983 0.8769 0.9037 0.8586 0.9118 avila 0.9357 0.9394 0.9564 0.9784 0.9422 0.9697 0.9816 F-measure phoneme 0.7479 0.7394 0.7528 0.7602 0.7627 0.7564 0.7586 satimage 0.9146 0.9411 0.9374 0.9319 0.9414 0.9398 0.9461 pen 0.9430 0.9718 0.9586 0.9676 0.9755 0.9563 0.9681 wine 0.6084 0.5885 0.5759 0.5605 0.5945 0.5722 0.5966 letter 0.8721 0.8898 0.8646 0.8571 0.8772 0.8519 0.8936 avila 0.9400 0.9483 0.9411 0.9576 0.9280 0.9538 0.9511 G-mean phoneme 0.8241 0.8157 0.8356 0.8398 0.8422 0.8394 0.8486 satimage 0.9521 0.9650 0.9669 0.9596 0.9718 0.9721 0.9778 pen 0.9749 0.9888 0.9871 0.9881 0.9908 0.9783 0.9902 wine 0.7510 0.7286 0.7661 0.7483 0.7720 0.7681 0.7897 letter 0.9432 0.9324 0.9409 0.9301 0.9446 0.9207 0.9500 avila 0.9642 0.9668 0.9735 0.9853 0.9656 0.9810 0.9859 AUC phoneme 0.8271 0.8197 0.8366 0.8410 0.8432 0.8402 0.8486 satimage 0.9529 0.9655 0.9673 0.9602 0.9720 0.9724 0.9779 pen 0.9751 0.9888 0.9871 0.9881 0.9909 0.9785 0.9902 wine 0.7700 0.7533 0.7765 0.7621 0.7829 0.7775 0.7963 letter 0.9442 0.9342 0.9419 0.9317 0.9456 0.9230 0.9508 avila 0.9646 0.9672 0.9737 0.9854 0.9659 0.9811 0.9860