A simple weighting scheme for classification in two-group discriminant problems

Computers and Operations Research

Volumn 30, Issue 1, 2003, Pages 155-164

  Fung Lam, Kim ^a   W Moy, Jane ^b  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^b HONG KONG BAPTIST UNIVERSITY   (Hong Kong)

Author keywords

Classification; Discriminant analysis; Linear programming; Statistics

Indexed keywords

COMPUTER SIMULATION; DECISION MAKING; LINEAR PROGRAMMING; PROBLEM SOLVING; STATISTICAL METHODS;

DISCRIMINANT ANALYSIS;

OPERATIONS RESEARCH;

DISCRIMINANT ANALYSIS;

EID: 0037211216     PISSN: 03050548     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0305-0548(01)00089-2     Document Type: Article

References (19)

1. Issues in the use and interpretation of discriminant analysis
2. Mathematical programming approaches for the classification problem in two-group discriminant problem
3. The use of multiple measurements in taxonomic problems
4. An experimental comparison of statistical and linear programming approaches to the discriminant problem
5. Evaluating alternative linear programming models to solve the two-group discriminant problem
6. Four approaches to the classification problem in discriminant analysis: An experimental study
7. An experimental comparison of some recently developed linear programming approaches to the discriminant problem
8. Simple but powerful goal programming models for discriminant problems
9. Considerations for mathematical programming models in discriminant analysis
10. Survey of mathematical programming models and experimental results for linear discriminant analysis
11. p-norm methods
12. An experimental comparison of several approaches to the discriminant problem with both qualitative and quantitative variables
13. On the performance of linear programming heuristics applied on a quadratic transformation in the classification problem
14. Resolving certain difficulties and improving the classification power of LP discriminant analysis formulations
15. Improved linear programming models for discriminant analysis
16. Minimizing deviations from the group mean: A new linear programming approach for the two-group classification problem
17. A comparison of the performance of three classification procedures when applied to contaminated data
18. On the classification gap in MP-based approaches to the discriminant problem