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ICCBR
2003
Springer

Case-Based Ranking for Decision Support Systems

10 years 4 months ago
Case-Based Ranking for Decision Support Systems
Abstract. 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.
Paolo Avesani, Sara Ferrari, Angelo Susi
Added 06 Jul 2010
Updated 06 Jul 2010
Type Conference
Year 2003
Where ICCBR
Authors Paolo Avesani, Sara Ferrari, Angelo Susi
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