Ensemble ranking: Aggregation of rankings produced by different multi-criteria decision-making methods - Mohammadi, Majid; Rezaei, Jafar (Omega (Oxford), 2020)
•A new approach for ensemble ranking is proposed based on the half-quadratic theory.•The proposed method assigns weights to the MCDM methods being used objectively.•The proposed ensemble ranking enables us to compute a consensus index and a trust level for the final aggregated ranking.•As a real-world implementation, we study the ranking of ontology alignment systems with respect to multiple performance metrics. One of the essential problems in multi-criteria decision-making (MCDM) is ranking a set of alternatives based on a set of criteria. In this regard, there exist several MCDM methods which rank the alternatives in different ways. As such, it would be worthwhile to try and arrive at a consensus on this important subject. In this paper, a new approach is proposed based on the half-quadratic (HQ) theory. The proposed approach determines an optimal weight for each of the MCDM ranking methods, which are used to compute the aggregated final ranking. The weight of each ranking method is obtained via a minimizer function that is inspired by the HQ theory, which automatically fulfills the basic constraints of weights in MCDM. The proposed framework also provides a consensus index and a trust level for the aggregated ranking. To illustrate the proposed approach, the evaluation and comparison of ontology alignment systems are modeled as an MCDM problem and the proposed framework is applied to the ontology alignment evaluation initiative (OAEI) 2018, for which the ranking of participating systems is of the utmost importance.