Trace-Based Weighting Approach for Multiple Criteria Decision Making

Hoang Nam Ho (1) , Mourad Rabah (1) , Samuel Nowakowski (2) , Pascal Estraillier (1)

1 L3I - Laboratoire Informatique, Image et Interaction - EA 2118
2 KIWI - Knowledge Information and Web Intelligence

Decision-making has evolved as an interesting research problem for decision community. We consider a decision problem that takes into account several criteria called Multiple Criteria Decision Making (MCDM) in an interactive application for adaptive execution. In this paper, we present a method for automatically weighting criteria generation based on users' traces. In our method, we suggest a process that contains all steps describing alternately what is necessary to prepare in order to weight all criteria. We propose a modified method using Naïve Bayes network to exploit the traces (the past of users), there will be used as information for estimating the score of criteria. Experimental results are presented to illustrate a full process and an automatic generation of weighting criteria by a set of values.

Mots clés: criteria weighting; interactive adaptive system; Multiple Criteria Decision Making; Naive Bayes; traces

Domaines: Intelligence artificielle [cs.AI]

Hoang Nam Ho, Mourad Rabah, Samuel Nowakowski, Pascal Estraillier. Trace-Based Weighting Approach for Multiple Criteria Decision Making. Journal of Software, 2014, 9 (8), pp.2180-2187. ⟨hal-01058156⟩