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Solving the Classification Problem in Discriminant Analysis Via Linear and Nonlinear Programming Methods *
Author(s) -
Stam Antonie,
Joachimsthaler Erich A.
Publication year - 1989
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1989.tb01878.x
Subject(s) - linear discriminant analysis , optimal discriminant analysis , computer science , linear programming , discriminant , extension (predicate logic) , mathematical optimization , nonlinear system , monte carlo method , nonlinear programming , machine learning , artificial intelligence , algorithm , mathematics , statistics , physics , quantum mechanics , programming language
This paper demonstrates the feasibility of applying nonlinear programming methods to solve the classification problem in discriminant analysis. The application represents a useful extension of previously proposed linear programming‐based solutions for discriminant analysis. The analysis of data obtained by conducting a Monte Carlo simulation experiment shows that these new procedures are promising. Future research that should promote application of the proposed methods for solving classification problems in a business decision‐making environment is discussed.