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 -

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