A simple and robust scoring technique for binary classification

Charles Gomes, Hisham Noçairi, Marie Thomas, Jean-François Collin, Gilbert Saporta


A new simple scoring technique is developed in a binary supervised classification context when only a few observations areavailable. It consists in two steps: in the first one partial scores are obtained, one for each predictor, either categorical orcontinuous. Each partial score is a discrete variable with 7 values ranging from -3 to 3, based upon an empirical comparison ofthe distributions for each class. In a second step the partial scores are added and standardised into a global score, which allowsa decision rule.This simple technique is successfully compared with classical supervised techniques for a classical benchmark and has beenproved to be especially well fitted in an industrial problem.

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DOI: https://doi.org/10.5430/air.v3n1p52


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Artificial Intelligence Research

ISSN 1927-6974 (Print)   ISSN 1927-6982 (Online)

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