Integer programming methods for normalisation and variable selection in mathematical programming discriminant analysis models

By John J. Glen. 1999

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Supersparse linear integer models for optimized medical scoring systems (2015, background): “One unique benefit in minimizing an approximation-free objective function over a finite set of discrete coefficients is that the free parameters in the objective of (2) have special properties.”
Methods and Models for Interpretable Linear Classification (2014, methodology): “Recent progress in commercial MIP software has made it possible to solve exponentially larger problems [13, 12], and ushered in new MIP classification models that involve feature selection [42, 45, 46, 84], or the creation of a reserved-judgement region [22, 21].”
Recent developments in consumer credit risk assessment (2007, methodology): “Work by Glen (1999) has amended the algorithm to avoid some of these problems.”
Multicriteria classification and sorting methods: A literature review (2002, background): “For instance, Glover (1990) proposed a general hybrid MP formulation considering the maximum violation r i , the sum of all r i , the minimum deviational variables d i and the sum of all d i .”