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Nearest Labelset Using Double Distances for Multi-label Classification
[article]
2017
arXiv
pre-print
Multi-label classification is a type of supervised learning where an instance may belong to multiple labels simultaneously. Predicting each label independently has been criticized for not exploiting any correlation between labels. In this paper we propose a novel approach, Nearest Labelset using Double Distances (NLDD), that predicts the labelset observed in the training data that minimizes a weighted sum of the distances in both the feature space and the label space to the new instance. The
arXiv:1702.04684v1
fatcat:hgqpjv3aijejpmblncgnk7zuz4