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Avtomatika i Telemekhanika, 2022, Issue 12, Pages 78–88
DOI: https://doi.org/10.31857/S0005231022120078 (Mi at16098) 		 
This article is cited in 1 scientific paper (total in 1 paper)

Topical issue

Method for improving gradient boosting learning efficiency based on modified loss functions

N. S. Koroleva, O. V. Senkob

a Lomonosov Moscow State University, Moscow, 119991 Russia
b Federal Research Center “Computer Science and Control,” Russian Academy of Sciences, Moscow, 119333 Russia
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DOI: https://doi.org/10.31857/S0005231022120078
Abstract: We consider a new method to improve the quality of training in gradient boosting as well as to increase its generalization performance based on the use of modified loss functions. In computational experiments, the possible applicability of this method to improve the quality of gradient boosting when solving various classification and regression problems on real data is shown.
Keywords: gradient boosting, decision tree, loss function, machine learning, data analysis.
Presented by the member of Editorial Board: A. A. Lazarev

Received: 31.01.2022
Revised: 21.06.2022
Accepted: 29.06.2022
English version:
Automation and Remote Control, 2022, Volume 83, Issue 12, Pages 1935–1943
DOI: https://doi.org/10.1134/S00051179220120074
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: N. S. Korolev, O. V. Senko, “Method for improving gradient boosting learning efficiency based on modified loss functions”, Avtomat. i Telemekh., 2022, no. 12, 78–88; Autom. Remote Control, 83:12 (2022), 1935–1943
Citation in format AMSBIB
\Bibitem{KorSen22}
\by N.~S.~Korolev, O.~V.~Senko
\paper Method for improving gradient boosting learning efficiency based on modified loss functions
\jour Avtomat. i Telemekh.
\yr 2022
\issue 12
\pages 78--88
\mathnet{http://mi.mathnet.ru/at16098}
\crossref{https://doi.org/10.31857/S0005231022120078}
\edn{https://elibrary.ru/KSLBAE}
\transl
\jour Autom. Remote Control
\yr 2022
\vol 83
\issue 12
\pages 1935--1943
\crossref{https://doi.org/10.1134/S00051179220120074}
Linking options:
  • https://www.mathnet.ru/eng/at16098
  • https://www.mathnet.ru/eng/at/y2022/i12/p78
    • This publication is cited in the following 1 articles:
    1. Ying Song, Danjing Li, “Application of a Novel Data-Driven Framework in Anomaly Detection of Industrial Data”, IEEE Access, 12 (2024), 102798  crossref
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    		Avtomatika i Telemekhanika
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    Abstract page:133
    References:30
    First page:19
     
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