Several techniques can be used to solve multi-criteria decision making (MCDM) problems and to provide a global ranking of the alternatives considered. However, in a context with a high number of alternatives and where decision criteria relate to soft goals, the decision problem is particularly hard to solve. This paper analyzes the use of artificial neural networks to improve the relevance of the ranking of alternatives delivered by MCDM problem-solving techniques. Afterwards, a model using a combination of artificial neural networks and of the weighted sum model, a particular MCDM problem-solving technique, is built to recommend smartphones.
|Number of pages||6|
|Publication status||Published - 1 Jan 2018|
|Event|| 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018) - Bruges, Bruges, Belgium|
Duration: 25 Apr 2018 → 27 Apr 2018
|Conference||26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018)|
|Period||25/04/18 → 27/04/18|
Amaral De Sousa, V., 2017
Student thesis: Master types › Master in Business Engineering Professional focus in Analytics & Digital Business
Amaral De Sousa, V., Simonofski, A., Snoeck, M., & Jureta, I. (2018). Structuring and Solving Multi-Criteria Decision Making Problems using Artificial Neural Networks: A Smartphone Recommendation Case. 165-170. Paper presented at 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018), Bruges, Belgium.