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This article is cited in 8 scientific papers (total in 8 papers)
INFORMATICS
Two-level regression method using ensembles of trees with optimal divergence
Yu. I. Zhuravleva, O. V. Sen'koa, A. A. Dokukina, N. N. Kiselyovab, I. A. Saenkoc a Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, Moscow, Russia
b Baikov Institute of Metallurgy and Materials Science, Russian Academy of Sciences, Moscow, Russia
c Lomonosov Moscow State University, Moscow, Russia
Abstract:
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.
Received: 17.06.2021 Revised: 17.06.2021 Accepted: 19.06.2021
Citation:
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”, Dokl. RAN. Math. Inf. Proc. Upr., 499 (2021), 63–66; Dokl. Math., 104:1 (2021), 212–215
Linking options:
https://www.mathnet.ru/eng/danma192 https://www.mathnet.ru/eng/danma/v499/p63
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Abstract page: | 118 | Full-text PDF : | 29 | References: | 20 |
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