Annals of Operations Research

, Volume 166, Issue 1, pp 23–38 | Cite as

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



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.


Multi-class classification Ranking “Max-Win” voting Fuzzy voting 


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Department of Computing and SoftwareMcMaster UniversityHamiltonCanada
  2. 2.Department of Industrial & Enterprise Systems EngineeringUniversity of Illinois at Urbana-ChampaignUrbanaUSA

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