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A local boosting algorithm for solving classification problems

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  • Zhang, Chun-Xia
  • Zhang, Jiang-She

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  • Zhang, Chun-Xia & Zhang, Jiang-She, 2008. "A local boosting algorithm for solving classification problems," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1928-1941, January.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:4:p:1928-1941
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    References listed on IDEAS

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    1. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
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    Cited by:

    1. Bernd Bischl & Julia Schiffner & Claus Weihs, 2013. "Benchmarking local classification methods," Computational Statistics, Springer, vol. 28(6), pages 2599-2619, December.
    2. Jasdeep S. Banga & B. Wade Brorsen, 2019. "Profitability of alternative methods of combining the signals from technical trading systems," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 26(1), pages 32-45, January.
    3. Martinez, Waldyn & Gray, J. Brian, 2016. "Noise peeling methods to improve boosting algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 483-497.
    4. Zhang, Chun-Xia & Zhang, Jiang-She & Zhang, Gai-Ying, 2009. "Using Boosting to prune Double-Bagging ensembles," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1218-1231, February.
    5. Chun-Xia Zhang & Guan-Wei Wang & Jiang-She Zhang, 2012. "An empirical bias--variance analysis of DECORATE ensemble method at different training sample sizes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(4), pages 829-850, September.
    6. Zhang, Mingzhu & He, Changzheng & Gu, Xin & Liatsis, Panos & Zhu, Bing, 2013. "D-GMDH: A novel inductive modelling approach in the forecasting of the industrial economy," Economic Modelling, Elsevier, vol. 30(C), pages 514-520.
    7. Rokach, Lior, 2009. "Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4046-4072, October.
    8. Chun-Xia Zhang & Guan-Wei Wang & Jun-Min Liu, 2015. "RandGA: injecting randomness into parallel genetic algorithm for variable selection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(3), pages 630-647, March.

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