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A unified view of class-selection with probabilistic classifiers
2014
Pattern Recognition
The possibility of selecting a subset of classes instead of one unique class for assignation is of great interest in many decision making systems. Selecting a subset of classes instead of singleton allows to reduce the error rate and to propose a reduced set to another classifier or an expert. This second step provides additional information, and therefore increases the quality of the result. In this paper, a unified view of the problem of class-selection with probabilistic classifiers is
doi:10.1016/j.patcog.2013.07.020
fatcat:fy3jg2unlvg4taweuo4o6fzv34