An iterative mixed integer programming method for classification accuracy maximizing discriminant analysis

DOI10.1016/S0305-0548(01)00088-0zbMath1022.90010OpenAlexW2014076886MaRDI QIDQ1869895

J. J. Glen

Publication date: 28 April 2003

Published in: Computers \& Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0305-0548(01)00088-0

zbMATH Keywords

discriminant analysismathematical programmingvariable selection

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Mixed integer programming (90C11)

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Uses Software

• UCI-ml
• SPSS
• SAS
• AdaBoost.MH
• SAS/STAT

Cites Work

• A comparison of a robust mixed-integer approach to existing methods for establishing classification rules for the discriminant problem
• Simple but powerful goal programming models for discriminant problems
• An improved neural classification network for the two-group problem
• Improper linear discriminant classifiers
• Integer programming methods for normalisation and variable selection in mathematical programming discriminant analysis models
• Managerial Applications of Neural Networks: The Case of Bank Failure Predictions
• Classification accuracy in discriminant analysis: a mixed integer programming approach
• A comparison of the classical and the linear programming approaches to the classification problem in discriminant analysis
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