An experimental comparison of the new goal programming and the linear programming approaches in the two-group discriminant problems

Authors:
Hasan Bal;H. Hasan Örkcü;Salih Çelebioglu
Affiliations:
Department of Statistics, Faculty of Arts and Sciences, Gazi University, Teknikokullar, Ankara, Turkey;Department of Statistics, Faculty of Arts and Sciences, Gazi University, Teknikokullar, Ankara, Turkey;Department of Statistics, Faculty of Arts and Sciences, Gazi University, Teknikokullar, Ankara, Turkey
Venue:
Computers and Industrial Engineering
Year:
2006

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Abstract

The aim of this article is to consider a new linear programming and two goal programming models for two-group classification problems. When these approaches are applied to the data of real life or of simulation, our proposed new models perform well both in separating the groups and the group-membership predictions of new objects. In discriminant analysis some linear programming models determine the attribute weights and the cut-off value in two steps, but our models determine simultaneously all of these values in one step. Moreover, the results of simulation experiments show that our proposed models outperform significantly than existing linear programming and statistical approaches in attaining higher average hit-ratios.