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.
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The research of the last author was supported by the grant #R.PG 0048923 of NESERC, the MITACS project “New Interior Point Methods and Software for Convex Conic-Linear Optimization and Their Application to Solve VLSI Circuit Layout Problems” and the Canada Researcher Chair Program.
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Jiao, T., Peng, J. & Terlaky, T. A confidence voting process for ranking problems based on support vector machines. Ann Oper Res 166, 23–38 (2009). https://doi.org/10.1007/s10479-008-0410-6
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DOI: https://doi.org/10.1007/s10479-008-0410-6