Author/Authors :
Izadi، B. نويسنده Department of Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Iran , , Ranjbarian، B. نويسنده Department of Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Iran. , , Ketabi، S. نويسنده Department of Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Iran , , Nassiri-Mofakham، F. نويسنده Department of Information Technology Engineering, Faculty of Engineering, University of Isfahan, Iran ,
Abstract :
Among various statistical and data mining discriminant analysis proposed so far for group
classification, linear programming discriminant analysis has recently attracted the
researchers’ interest. This study evaluates multi-group discriminant linear programming
(MDLP) for classification problems against well-known methods such as neural networks and
support vector machine. MDLP is less complicated as compared to other methods and does not
suffer from having local optima. This study also proposes a fuzzy Delphi method to select and
gather the required data, when databases suffer from deficient data. In addition, to absorb the
uncertainty infused to collecting data, interval MDLP (IMDLP) is developed. The results show
that the performance of MDLP and specially IMDLP is better than conventional classification
methods with respect to correct classification, at least for small and medium-size datasets.
