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A confidence voting process for ranking problems based on support vector machines
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AI
DOI：10.1007/s10479-008-0410-6.png)
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Abstract
En 中文
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.
Keywords:
Multi-class classification
Ranking
Max-Win voting
Fuzzy voting
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