TY  - JOUR
ID  - jf:Neurocomputing
T1  - Efficient Voting Prediction for Pairwise Multilabel Classification
A1  - Loza Mencía, Eneldo
A1  - Park, Sang-Hyeun
A1  - Fürnkranz, Johannes
JA  - Neurocomputing
Y1  - 2010
VL  - 73
IS  - 7-9
SP  - 1164
EP  - 1176
SN  - 0925-2312
UR  - http://www.ke.tu-darmstadt.de/publications/papers/neucom10.pdf
M2  - doi: 10.1016/j.neucom.2009.11.024
KW  - efficient classification
KW  - learning by pairwise comparison
KW  - multilabel classification
KW  - voting aggregation
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   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.
M1  - note2={Volume: Advances in Computational Intelligence and Learning - 17th European Symposium on Artificial Neural Networks 2009
M1  -  17th European Symposium on Artificial Neural Networks 2009}
ER  -