.png){kind=icon}
返回
Binary relevance for multi-label learning: an overview
.png){kind=icon}
PRE
AI
DOI：10.1007/s11704-017-7031-7.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 relevance aimed at issues other than label correlation exploitation are introduced. As a conclusion, we provide suggestions on future research directions.
Keyword:
machine learning
multi-label learning
binary relevance
label correlation
class-imbalance
relative labeling-importance
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.6
论文数:
1.6K
被引数:
2.8K
机构
引用论文
Learning Label-Specific Features and Class-Dependent Labels for Multi-Label Classification用于多标签分类的学习标签特定特征和类别相关标签
PREAI
分享收藏
分享收藏Gene Expression Analysis of the Brazilian Type of Hereditary Persistence of Fetal Hemoglobin: Identification of Genes that Could be Related to γ-Globin Activation.png)
Hemoglobin
HemoglobinIF0
PREAI
分享收藏
查看更多内容