Performance Analysis of Classification Methods and Alternative Linear Programming Integrated with Fuzzy Delphi Feature Selection
Автор: Bahram Izadi, Bahram Ranjbarian, Saeedeh Ketabi, Faria Nassiri-Mofakham
Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs
Статья в выпуске: 10 Vol. 5, 2013 года.
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Among various statistical and data mining discriminant analysis proposed so far for group classification, linear programming discriminant analysis have 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, support vector machine, and so on. MDLP is less complex compared to other methods and does not suffer from local optima. However, sometimes classification becomes infeasible due to insufficient data in databases such as in the case of an Internet Service Provider (ISP) small and medium-sized market considered in this research. This study proposes a fuzzy Delphi method to select and gather required data. The results show that the performance of MDLP is better than other methods with respect to correct classification, at least for small and medium-sized datasets.
Fuzzy Delphi Feature Selection, Customer Classification Problem, Multi-Group Linear Programming, Artificial Neural Network, Logistic Regression, Support Vector Machine
Короткий адрес: https://sciup.org/15011974
IDR: 15011974
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