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[GNT14] A simple and robust scoring technique for binary classification
Revue Internationale avec comité de lecture : Journal Artificial Intelligence Research, vol. 3(1), pp. 52-58, 2014, (doi:10.5430/air.v3n1p52)Mots clés: Binary outcome, Prediction, Scores, Small learning set
Résumé:
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.
Equipe:
msdma
BibTeX
| @article { | |||
| GNT14, | |||
| title | = | "{A simple and robust scoring technique for binary classification}", | |
| author | = | "C. Gomes and H. Nocairi and M. Thomas and J. Collin and G. Saporta", | |
| journal | = | "Artificial Intelligence Research", | |
| year | = | 2014, | |
| volume | = | 3, | |
| number | = | 1, | |
| pages | = | "52-58", | |
| doi | = | "10.5430/air.v3n1p52", | |
| } | |||
