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A confidence voting process for ranking problems based on support vector machines

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  • Tianshi Jiao
  • Jiming Peng
  • Tamás Terlaky

Abstract

In this paper, we deal with ranking problems arising from various data mining applications where the major task is to train a rank-prediction model to assign every instance a rank. We first discuss the merits and potential disadvantages of two existing popular approaches for ranking problems: the ‘Max-Wins’ voting process based on multi-class support vector machines (SVMs) and the model based on multi-criteria decision making. We then propose a confidence voting process for ranking problems based on SVMs, which can be viewed as a combination of the SVM approach and the multi-criteria decision making model. Promising numerical experiments based on the new model are reported. Copyright Springer Science+Business Media, LLC 2009

Suggested Citation

  • Tianshi Jiao & Jiming Peng & Tamás Terlaky, 2009. "A confidence voting process for ranking problems based on support vector machines," Annals of Operations Research, Springer, vol. 166(1), pages 23-38, February.
    • Handle: RePEc:spr:annopr:v:166:y:2009:i:1:p:23-38:10.1007/s10479-008-0410-6
    DOI: 10.1007/s10479-008-0410-6
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    1. World Bank, 2002. "World Development Indicators 2002," World Bank Publications - Books, The World Bank Group, number 13921, December.
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    1. Andrew Kusiak & Xiupeng Wei, 2014. "Prediction of methane production in wastewater treatment facility: a data-mining approach," Annals of Operations Research, Springer, vol. 216(1), pages 71-81, May.
    2. Manuel Castejón-Limas & Joaquín Ordieres-Meré & Ana González-Marcos & Víctor González-Castro, 2011. "Effort estimates through project complexity," Annals of Operations Research, Springer, vol. 186(1), pages 395-406, June.
    3. Emilio Carrizosa & Belen Martin-Barragan, 2011. "Maximizing upgrading and downgrading margins for ordinal regression," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 74(3), pages 381-407, December.
    4. Ling Tang & Shuai Wang & Kaijian He & Shouyang Wang, 2015. "A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting," Annals of Operations Research, Springer, vol. 234(1), pages 111-132, November.
    5. Doumpos, Michael & Zopounidis, Constantin, 2011. "Preference disaggregation and statistical learning for multicriteria decision support: A review," European Journal of Operational Research, Elsevier, vol. 209(3), pages 203-214, March.

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