Nearest labelset using double distances for multi-label classification

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 article we propose a novel approach, Nearest Labelset using D...

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Bibliographic Details
Main Authors: Hyukjun Gweon, Matthias Schonlau, Stefan H. Steiner
Format: Article
Language:English
Published: PeerJ Inc. 2019-12-01
Series:PeerJ Computer Science
Subjects:
Online Access:https://peerj.com/articles/cs-242.pdf