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Analysis of ROBECO data by neural networks

Author

Listed:
  • Wojtek Kowalczyk
  • Rafal Weron

Abstract

Our 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.

Suggested Citation

  • Wojtek Kowalczyk & Rafal Weron, 1995. "Analysis of ROBECO data by neural networks," HSC Research Reports HSC/95/02, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    • Handle: RePEc:wuu:wpaper:hsc9502
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    File URL: http://www.im.pwr.wroc.pl/~hugo/RePEc/wuu/wpaper/HSC_95_02.pdf
    File Function: Original version, 1995
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    More about this item

    Keywords

    Neural network; Classification; Client satisfaction;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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