基于Lightgbm和XGBoost的优化深度森林算法
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谢军飞, 张海清, 李代伟, 于曦, 邓钧予
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Optimized deep forest algorithm based on Lightgbm and XGBoost
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Junfei Xie, Haiqing Zhang, Daiwei Li, Xi Yu, Junyu Deng
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表2 LIGHT?XDF和十种对比算法在八个数据集上的算法精度比较
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Table 2 Classification accuracy of LIGHT?XDF and other ten algorithms on eight datasets
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| 数据集 | RF | NB | KNN | SVM | Lightgbm | XGBoost | ASTGNN | ada⁃mdf | lgb⁃df | Gcforest | LIGHT⁃XDF |
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| Chemical Engineering | 98.25% | 92.98% | 50.88% | 54.39% | 95.74% | 98.25% | 97.92% | 95.75% | 98.25% | 96.49% | 98.25% | | EEE | 95.24% | 92.38% | 56.19% | 53.33% | 94.29% | 97.14% | 96.25% | 96.55% | 94.29% | 98.10% | 99.05% | | Mechanical Engineering | 93.88% | 87.76% | 44.90% | 42.86% | 91.84% | 95.92% | 91.07% | 87.81% | 96.42% | 93.88% | 95.92% | | Adult | 84.33% | 81.10% | 84.33% | 79.36% | 86.94% | 87.14% | 94.25% | 85.40% | 86.91% | 86.29% | 86.95% | | Dry_Bean | 92.63% | 76.35% | 71.79% | 63.37% | 92.87% | 92.61% | 91.75% | 90.74% | 93.05% | 91.31% | 93.05% | | Bank Marketing | 90.68% | 84.70% | 88.37% | 88.56% | 91.04% | 90.71% | 92.61% | 90.59% | 91.07% | 90.75% | 90.87% | | Winequality⁃red | 66.46% | 55.21% | 51.25% | 51.67% | 65.21% | 66.25% | 72.36% | 64.00% | 67.71% | 63.33% | 68.54% | | Winequality⁃white | 66.12% | 45.58% | 46.94% | 43.27% | 67.01% | 66.05% | 63.74% | 58.94% | 65.03% | 57.21% | 66.40% |
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