ORCID: https://orcid.org/0000-0002-9944-4108; Waegeman, Willem und Welker, Volkmar
(2012):
Label Ranking with Partial Abstention based on Thresholded Probabilistic Models.
25. NIPS 2012, Lake Tahoe, Nevada, USA, December 3-8, 2012.
Advances in Neural Information Processing Systems.
Vol. 25
pp. 2510-2518
[PDF, 252kB]

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: | Conference or Workshop Item (Paper) |
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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 |
URN: | urn:nbn:de:bvb:19-epub-91821-7 |
Language: | English |
Item ID: | 91821 |
Date Deposited: | 06. Apr 2022 10:07 |
Last Modified: | 15. Oct 2024 12:09 |