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6 LIGHT?XDF和十种对比算法在八个数据集上的算法执行时间的比较(s)

Table 6 Execution time (s) 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 0.604 0.063 0.443 0.072 0.484 0.531 13.250 3.083 1.450 3.060 1.785 EEE 0.545 0.074 0.538 0.080 0.547 0.566 15.274 3.262 1.860 3.186 1.944 Mechanical Engineering 0.509 0.059 0.471 0.068 0.568 0.595 14.723 3.046 1.615 3.077 1.783 Adult 13.557 12.636 14.910 26.48 13.963 13.104 308.820 71.795 16.002 89.210 39.752 Dry_Bean 3.848 4.074 2.615 4.262 2.544 4.106 254.230 28.299 19.409 61.214 60.437 Bank Marketing 15.775 12.358 17.868 12.32 12.280 15.303 325.780 115.705 15.180 70.722 36.411 Winequality⁃red 0.719 0.147 0.567 0.215 0.872 0.974 43.152 5.672 5.539 5.475 8.712 Winequality⁃white 1.212 0.374 0.839 0.887 1.111 1.205 68.345 16.897 8.460 15.932 24.595