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:: Volume 4, Issue 1 (5-2013) ::
IJOR 2013, 4(1): 55-74 Back to browse issues page
Multi-Group Classification Using Interval Linea rProgramming
Izadi , Ranjbarian, Ketabi, Nassiri-Mofakham
Abstract:   (14512 Views)

  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.

Keywords: Multi-group interval linear programming, Classification problem, Fuzzy Delphi feature selection.
Full-Text [PDF 179 kb]   (7229 Downloads)    
Type of Study: Original |
Received: 2014/04/24 | Accepted: 2014/04/24 | Published: 2014/04/24
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Volume 4, Issue 1 (5-2013) Back to browse issues page
مجله انجمن ایرانی تحقیق در عملیات Iranian Journal of Operations Research
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