A New Instance-Based Label Ranking Approach Using the Mallows Model

Cheng, Weiwei and Hüllermeier, Eyke (2009): A New Instance-Based Label Ranking Approach Using the Mallows Model. In: Yu, Wen; He, Haibo and Zhang, Nian (eds.) : (2009): Advances in Neural Networks – ISNN 2009 : 6th International Symposium on Neural Networks, ISNN 2009 Wuhan, China, May 26-29, 2009 Proceedings, Part I. Lecture Notes in Computer Science, Vol. 5551. Berlin, Heidelberg: Springer. pp. 707-716

Abstract

Several machine learning methods allow for abstaining from uncertain predictions. While being common for settings like conventional classification, abstention has been studied much less in learning to rank. We address abstention for the label ranking setting, allowing the learner to declare certain pairs of labels as being incomparable and, thus, to predict partial instead of total orders. In our method, such predictions are produced via thresholding the probabilities of pairwise preferences between labels, as induced by a predicted probability distribution on the set of all rankings. We formally analyze this approach for the Mallows and the Plackett-Luce model, showing that it produces proper partial orders as predictions and characterizing the expressiveness of the induced class of partial orders. These theoretical results are complemented by experiments demonstrating the practical usefulness of the approach.

• Item Type: Book Section
• Faculties: Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning
• Subjects: 000 Computer science, information and general works > 004 Data processing computer science
• ISBN: 978-3-642-01506-9 ; 978-3-642-01507-6
• Place of Publication: Berlin, Heidelberg
• Language: English