Abstract
Building a decision support system (DSS) using small data sets usually results in uncertain knowledge, likely leading to incorrect decisions and causing a large losses. However, gathering sufficient samples for building a DSS often has significant costs in many cases. To solve this problem, a case study of a particular business decision-making procedure in which only small data sets are available is discussed. The learning accuracy for the modeling phase in the DSS was improved using the mega-trend-diffusion technique, which includes two learning tools: Back-propagation network and Bayesian network. The case study, a business diversification decision for an oil company, shows that the proposed technique contributes to increasing the prediction precision using very limited experience.
Original language | English |
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Pages (from-to) | 2525-2533 |
Number of pages | 9 |
Journal | Expert Systems With Applications |
Volume | 36 |
Issue number | 2 PART 1 |
DOIs | |
Publication status | Published - 2009 Mar 1 |
All Science Journal Classification (ASJC) codes
- Engineering(all)
- Computer Science Applications
- Artificial Intelligence