基于Lightgbm和XGBoost的优化深度森林算法

谢军飞, 张海清, 李代伟, 于曦, 邓钧予

Optimized deep forest algorithm based on Lightgbm and XGBoost

Junfei Xie, Haiqing Zhang, Daiwei Li, Xi Yu, Junyu Deng

表4 LIGHT?XDF和十种对比算法在八个数据集上的算法的F1比较

Table 4 F1 of LIGHT?XDF and other ten algorithms on eight datasets

数据集 RF NB KNN SVM Lightgbm XGBoost ASTGNN ada⁃mdf lgb⁃df Gcforest LIGHT⁃XDF Chemical Engineering 0.980 0.927 0.432 0.383 0.937 0.979 0.963 0.938 0.980 0.963 0.980 EEE 0.938 0.920 0.531 0.371 0.942 0.973 0.988 0.959 0.939 0.976 0.990 Mechanical Engineering 0.910 0.851 0.442 0.257 0.899 0.949 0.934 0.825 0.958 0.910 0.950 Adult 0.839 0.819 0.841 0.756 0.865 0.866 0.924 0.848 0.864 0.857 0.864 Dry_Bean 0.926 0.761 0.716 0.600 0.928 0.926 0.942 0.908 0.931 0.913 0.931 Bank Marketing 0.897 0.858 0.870 0.834 0.905 0.900 0.887 0.894 0.905 0.899 0.901 Winequality⁃red 0.646 0.539 0.490 0.471 0.638 0.647 0.625 0.616 0.663 0.606 0.668 Winequality⁃white 0.647 0.446 0.455 0.309 0.661 0.652 0.612 0.567 0.641 0.547 0.651