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Data Mining Based Model Aggregation

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  • Szucs, Imre

Abstract

Applying modelling techniques for getting acquainted with customer behaviour, predicting the customers’ next step is neccessary to keep in competition, by decreasing the capital requirement (Basel II - IRB) or making the portfolio more profitable. According to the easily implementable modelling techniques, data mining solutions widespread in practice. Using these models with no conditions can lead into inconsistent future on portfolio change. Consequence of this situation, contradictory predictions and conclusions come into existence. Recognizing and conscious handling of inconsistent predictions is an important task for experts working on different scene of the knowledge based economy and society. By realizing and solving the problem of inconsistency in modelling processes, the competitive advantage can be increased and strategic decisions can be supported by consistent predictions.

Suggested Citation

  • Szucs, Imre, 2007. "Data Mining Based Model Aggregation," GAZDÁLKODÁS: Scientific Journal on Agricultural Economics, Karoly Robert University College, vol. 51(Special E), pages 1-9.
    • Handle: RePEc:ags:gazdal:58928
    DOI: 10.22004/ag.econ.58928
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