Authors
Min-Ling Zhang, Yu-Kun Li, Xu-Ying Liu, Xin Geng
Publication date
2018/4
Source
Frontiers of Computer Science
Volume
12
Pages
191-202
Publisher
Higher Education Press
Description
Multi-label learning deals with problems where each example is represented by a single instance while being associated with multiple class labels simultaneously. Binary relevance is arguably the most intuitive solution for learning from multi-label examples. It works by decomposing the multi-label learning task into a number of independent binary learning tasks (one per class label). In view of its potential weakness in ignoring correlations between labels, many correlation-enabling extensions to binary relevance have been proposed in the past decade. In this paper, we aim to review the state of the art of binary relevance from three perspectives. First, basic settings for multi-label learning and binary relevance solutions are briefly summarized. Second, representative strategies to provide binary relevancewith label correlation exploitation abilities are discussed. Third, some of our recent studies on binary …
Total citations
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Scholar articles
ML Zhang, YK Li, XY Liu, X Geng - Frontiers of Computer Science, 2018