.png){kind=icon}
返回
A unified framework implementing linear binary relevance for multi-label learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2018.02.010.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 is the problem where each instance is associated with multiple labels simultaneously. Binary Relevance (BR), which comes from the idea of one-vs-all for multi-class classification, is a representative algorithm for multi-label learning. It ignores label correlations and may suffer the class-imbalance problem. However, BR is mostly implemented by decomposing into many independent binary classifiers and learning each individually, making it difficult to extend. Moreover, when extending BR by learning together, the mostly used least squared loss function is more suitable for a regression task rather than a classification task. In this paper, we propose a unified framework implementing linear BR for multi-label learning, which is easy to extend and can also be applied in one-vs-all for multi-class classification. We mainly focus on five popular convex loss functions. Experimental results show that the unified framework achieves competitive performances than traditional implementations and some other well-established algorithms for both multi-label learning and multi-class classification. Furthermore, logistic, exponential and least squared hinge loss functions are more suitable for multi-label learning, while logistic and least squared hinge loss functions are more suitable for multi-class classification. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Binary relevance
One-vs-all
Loss function
Multi-label learning
Multi-class classification
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:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance使用秩来避免方差分析中隐含的正态性假设
PREAI
分享收藏ML-KNN: A lazy learning approach to multi-label leamingMl-knn: 一种多标签学习的懒惰学习方法.png)
PATTERN RECOGNITION
OAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏分享收藏
查看更多内容