Case-Based Ranking for Decision Support Systems

Very often a planning problem can be formulated as a ranking problem: i.e. to find an order relation over a set of alternatives. The ranking of a finite set of alternatives can be designed as a preference elicitation problem. While the case-based preference elicitation approach is more effective with respect to the first principle methods, still the scaling problem remains an open issue because the elicitation effort has a quadratic relation with the number of alternative cases. In this paper we propose a solution based on the machine learning techniques. We illustrate how a boosting algorithm can effectively estimate pairwise preferences and reduce the effort of the elicitation process. Experimental results, both on artificial data and a real world problem in the domain of civil defence, showed that a good trade-off can be achieved between the accuracy of the estimated preferences, and the elicitation effort of the end user.