Linear Programming Computational Procedures for Ordinal Regression
Journal of the ACM (JACM)
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Gaussian Processes for Ordinal Regression
The Journal of Machine Learning Research
Preference learning with Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Magnitude-preserving ranking algorithms
Proceedings of the 24th international conference on Machine learning
Ranking with multiple hyperplanes
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Active exploration for learning rankings from clickthrough data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Classification with a Reject Option using a Hinge Loss
The Journal of Machine Learning Research
Interactive Multiobjective Optimization from a Learning Perspective
Multiobjective Optimization
Ranking with ordered weighted pairwise classification
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A general magnitude-preserving boosting algorithm for search ranking
Proceedings of the 18th ACM conference on Information and knowledge management
Incorporating robustness into web ranking evaluation
Proceedings of the 18th ACM conference on Information and knowledge management
The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List
The Journal of Machine Learning Research
Handbook of Multicriteria Analysis
Handbook of Multicriteria Analysis
Monotone Relabeling in Ordinal Classification
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Active learning to maximize accuracy vs. effort in interactive information retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Robust ordinal regression for multiple criteria group decision: UTAGMS-GROUP and UTADISGMS-GROUP
Decision Support Systems
Multiple Criteria Hierarchy Process in Robust Ordinal Regression
Decision Support Systems
Learning monotone nonlinear models using the Choquet integral
Machine Learning
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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.