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Feature selection is critical to knowledge-based authentication. In this paper, we adopt a wrapper method in which the learning machine is a generative probabilistic model, and the objective is to maximize the Kullback–Leibler divergence between the true empirical distribution defined by the legitimate knowledge and the approximating distribution representing an attacking strategy, both in the same...
Screening is a process for multiple criteria decision analysis (MCDA) that reduces a large set of alternatives to a smaller set that most likely contains the best choice. To study screening in detail, MCDA is first interpreted as consequence-based preference aggregation. Consequence data and preference expressions (values and weights) are defined and the aggregation steps are elaborated. Based on...
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