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A simple and robust scoring technique for binary classification

Abstract : A new simple scoring technique is developed in a binary supervised classification context when only a few observations are available. It consists in two steps: in the first one partial scores are obtained, one for each predictor, either categorical or continuous. Each partial score is a discrete variable with 7 values ranging from -3 to 3, based upon an empirical comparison of the distributions for each class. In a second step the partial scores are added and standardised into a global score, which allows a decision rule. This simple technique is successfully compared with classical supervised techniques for a classical benchmark and has been proved to be especially well fitted in an industrial problem.
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Charles Gomes, Hisham Nocairi, Marie Thomas, Jean-François Collin, Gilbert Saporta. A simple and robust scoring technique for binary classification. Journal of Artificial Intelligence Research, Association for the Advancement of Artificial Intelligence, 2014, 3 (1), pp.52-58. ⟨10.5430/air.v3n1p52⟩. ⟨hal-01126370⟩



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