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: | /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 |
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