Graded multilabel classification: the ordinal case

Cheng, Weiwei; Dembczyński, Krzysztof und Hüllermeier, Eyke (2010): Graded multilabel classification: the ordinal case. ICML '10: 27th International Conference on Machine Learning, Haifa, Israel, June 21 - 24, 2010. In: ICML'10: Proceedings of the 27th International Conference on Machine Learning, Madison, WI: Omnipress. pp. 223-230

Abstract

We propose a generalization of multilabel classification that we refer to as graded multilabel classification. The key idea is that, instead of requesting a yes-no answer to the question of class membership or, say, relevance of a class label for an instance, we allow for a graded membership of an instance, measured on an ordinal scale of membership degrees. This extension is motivated by practical applications in which a graded or partial class membership is natural. Apart from introducing the basic setting, we propose two general strategies for reducing graded multi-label problems to conventional (multilabel) classification problems. Moreover, we address the question of how to extend performance metrics commonly used in multilabel classification to the graded setting, and present first experimental results.

• Item Type: Conference or Workshop Item (Paper)
• 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
• Place of Publication: Madison, WI
• Annotation: ISBN 978-1-60558-907-7
• Language: English