Analysis of ROBECO data by neural networks
AbstractOur task was to find a model for classifying ROBECO clients into four classes according to their degree of satisfaction. Each client was represented by a vector of 30 variables, which could be split into two groups: variables related to the specific client and socio-geographical variables characterizing the area in which the client lived. The original set contained 21, 90, 288, and 146 vectors from group 1, 2, 3, and 4 respectively. Additionally, an independent validation set was provided with 3, 12, 48 and 32 vectors from corresponding groups.
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Bibliographic InfoPaper provided by Hugo Steinhaus Center, Wroclaw University of Technology in its series HSC Research Reports with number HSC/95/02.
Length: 21 pages
Date of creation: 1995
Date of revision:
Neural network; Classification; Client satisfaction;
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- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
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