ID  - jf:ESANN-09
T1  - Efficient Voting Prediction for Pairwise Multilabel Classification
A1  - Loza Mencía, Eneldo
A1  - Park, Sang-Hyeun
A1  - Fürnkranz, Johannes
TI  - Proceedings of the 17th European Symposium on Artificial Neural Networks (ESANN 2009, Bruges, Belgium)
Y1  - 2009
SP  - 117
EP  - 122
PB  - d-side publications
SN  - 2-930307-09-9
UR  - http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2009-112.pdf
N2  - 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   dn log n, where n is the total number of labels and d is the average number of labels, which is typically quite small in real-world datasets.
M1  - opturl={"http://www.ke.tu-darmstadt.de/publications/papers/ESANN09.pdf"}
ER  -