.png){kind=icon}
返回
Learning from multiple inconsistent and dependent annotators to support classification tasks
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2020.10.045.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Training a supervised learning model requires a labeled dataset, where the labeling process is usually carried out by an expert to provide the ground truth/gold standard for each sample. However, in many applications, such a gold standard is not available. Instead, several real-world scenarios give us access to annotations provided by crowds, holding different and unknown levels of expertise. Thus, Learning from crowds is a subject undergoing intense study, and its main aim is to manage various machine learning paradigms in the presence of multiple annotators. Most of the state-of-the-art approaches reside on two key assumptions: i) the labeler's performance does not depend on the input feature space, and ii) independence among the annotators is imposed. Here, we introduce a localized kernel alignment based annotator relevance analysis (LKAAR) to code each labeler's expertise as the matching between the feature and label spaces. Namely, LKAAR takes into account inter-annotators dependencies and models labelers' knowledge as a function of the input feature space. Experimental results devoted to classification, show that LKAAR achieves suitable performances from inconsistent labelers, even if the gold standard is not available. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
Learning from crowds
Localized kernel alignment
Inconsistent annotators
Dependent labelers
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
.png){kind=icon}
查看更多内容