Robust ordinal regression in preference learning and ranking

Abstract

Multiple Criteria Decision Aiding (MCDA) offers a diversity of approaches designed for providing the decision maker (DM) with a recommendation concerning a set of alternatives (items, actions) evaluated from multiple points of view, called criteria. This paper aims at drawing attention of the Machine Learning (ML) community upon recent advances in a representative MCDA methodology, called Robust Ordinal Regression (ROR). ROR learns by examples in order to rank a set of alternatives, thus considering a similar problem as Preference Learning (ML-PL) does. However, ROR implements the interactive preference construction paradigm, which should be perceived as a mutual learning of the model and the DM. The paper clarifies the specific interpretation of the concept of preference learning adopted in ROR and MCDA, comparing it to the usual concept of preference learning considered within ML. This comparison concerns a structure of the considered problem, types of admitted preference information, a character of the employed preference models, ways of exploiting them, and techniques to arrive at a final ranking.

Post factum analysis for robust multiple criteria ranking and sorting

“…Indeed, analysis of the robust results provokes reaction of the DM who may add a new or revise the old preference information. As noted by Corrente et al [8], such an interactive process ends when the yielded necessary, possible, or extreme recommendation is decisive and convincing for the DM.…”

Revealed preference theory: An algorithmic outlook

“…This type of approach has been extensively investigated, in particular, by researchers interested in multiple criteria problems with discrete alternatives (MCDA) (see, e.g., , and in particular Bouyssou and Pirlot (2016); Dyer (2016); Moretti et al (2016); Siskos et al (2016) for recent surveys of closely related topics; see also Corrente et al (2016) for extensions), or in conjoint analysis (see, e.g., Giesen et al (2010); Gustafsson et al (2007);Rao (2014)). More recently, similar questions have also been investigated in preference learning, a subfield of artificial intelligence (see, e.g., Corrente et al (2013);Fürnkranz and Hüllermeier (2010)). …”

Interactive Evolutionary Multiple Objective Optimization for Group Decision Incorporating Value-based Preference Disaggregation Methods

“…They can be perceived as hybrids combining the evolutionary method, called Non-dominated Sorting Genetic Algorithm II (NSGA-II) (Deb et al 2002), with some interactive ordinal regression approaches (see Jacquet-Lagrèze and Siskos 2001; Corrente et al 2013). Our interest in NEMO comes from its favorable characteristics in terms of both preference information and preference model it employs.…”

“…When there is no value function compatible with the preference information provided by all DMs, ε is interpreted as a non-statistical misranking error which indicates the distance between the DM's preferences and the recommendation which can be obtained for the assumed model (Corrente et al 2013). Obviously, in case of incompatibility, some pairwise comparisons are not reproduced by U R E P−E P S D .…”