[BibTeX] [RIS]
Efficient Voting Prediction for Pairwise Multilabel Classification
Type of publication: Article
Citation: jf:Neurocomputing
Journal: Neurocomputing
Volume: 73
Number: 7-9
Year: 2010
Month: March
Pages: 1164 - 1176
ISSN: 0925-2312
URL: http://www.ke.tu-darmstadt.de/publications/papers/neucom10.pdf
DOI: 10.1016/j.neucom.2009.11.024
Abstract: The pairwise approach to multilabel classification reduces the problem to learning and aggregating preference predictions among the possible labels. A key problem is the need to query a quadratic number of preferences for making a prediction. To solve this problem, we extend the recently proposed QWeighted algorithm for efficient pairwise multiclass voting to the multilabel setting, and evaluate the adapted algorithm on several real-world datasets. We achieve an average-case reduction of classifier evaluations from n^2 to n + n d log n, where n is the total number of possible labels and d is the average number of labels per instance, which is typically quite small in real-world datasets.
Userfields: note2={Volume: Advances in Computational Intelligence and Learning - 17th European Symposium on Artificial Neural Networks 2009, 17th European Symposium on Artificial Neural Networks 2009}
Keywords: efficient classification, learning by pairwise comparison, multilabel classification, voting aggregation
Authors Loza Mencía, Eneldo
Park, Sang-Hyeun
Fürnkranz, Johannes
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