Geodesic
Two-level regression method using ensembles of trees with optimal divergence
Doklady Rossijskoj akademii nauk. Matematika, informatika, processy upravleniâ, Tome 499 (2021), pp. 63-66.

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The article discusses a new two-level regression analysis method in which a corrective procedure is applied to optimal ensembles of regression trees. Optimization is carried out based on the simultaneous achievement of the divergence of the algorithms in the forecast space and a good approximation of the data by individual algorithms of the ensemble. Simple averaging, random regression forest, and gradient boosting are used as corrective procedures. Experiments are presented comparing the proposed method with the standard decision forest and the standard gradient boosting method for decision trees.
Keywords: regression, collective methods, bagging, gradient boosting. 
@article{DANMA_2021_499_a13,
     author = {Yu. I. Zhuravlev and O. V. Sen'ko and A. A. Dokukin and N. N. Kiselyova and I. A. Saenko},
     title = {Two-level regression method using ensembles of trees with optimal divergence},
     journal = {Doklady Rossijskoj akademii nauk. Matematika, informatika, processy upravleni\^a},
     pages = {63--66},
     publisher = {mathdoc},
     volume = {499},
     year = {2021},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/DANMA_2021_499_a13/}
}
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Yu. I. Zhuravlev; O. V. Sen'ko; A. A. Dokukin; N. N. Kiselyova; I. A. Saenko. Two-level regression method using ensembles of trees with optimal divergence. Doklady Rossijskoj akademii nauk. Matematika, informatika, processy upravleniâ, Tome 499 (2021), pp. 63-66. http://geodesic.mathdoc.fr/item/DANMA_2021_499_a13/

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