图 书 馆
基于社会上下文特征的多标签关系邻居分类Multi-Label Relational Neighbor Classification using Social Context Features |
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| 课程网址: | http://videolectures.net/kdd2013_wang_social_context/ |
| 主讲教师: | Xi Wang |
| 开课单位: | 中佛罗里达大学 |
| 开课时间: | 2013-09-27 |
| 课程语种: | 英语 |
| 中文简介: |  在本文中，我们提出了一种多标签迭代关系邻居分类器。采用社交情境功能（SCRN）。我们的分类器结合了从实例的社会特征获得的类传播概率分布，这些特征又从网络拓扑中提取。该类别传播概率捕获了节点属于每个类别的内在可能性，并在集合推断过程中汇总邻居的类别标签时充当每个类别的先验权重。在多个现实世界数据集上进行的实验表明，我们提出的分类器在网络化多标签数据上的分类性能优于常见基准。 |
| 课程简介: | Networked data, extracted from social media, web pages, and bibliographic databases, can contain entities of multiple classes, interconnected through different types of links. In this paper, we focus on the problem of performing multi-label classification on networked data, where the instances in the network can be assigned multiple labels. In contrast to traditional content-only classification methods, relational learning succeeds in improving classification performance by leveraging the correlation of the labels between linked instances. However, instances in a network can be linked for various causal reasons, hence treating all links in a homogeneous way can limit the performance of relational classifiers. In this paper, we propose a multi-label iterative relational neighbor classifier that employs social context features (SCRN). Our classifier incorporates a class propagation probability distribution obtained from instances' social features, which are in turn extracted from the network topology. This class-propagation probability captures the node's intrinsic likelihood of belonging to each class, and serves as a prior weight for each class when aggregating the neighbors' class labels in the collective inference procedure. Experiments on several real-world datasets demonstrate that our proposed classifier boosts classification performance over common benchmarks on networked multi-label data. |
| 关 键 词: | 数据集; 多标签; 网络数据 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-11-06：zyk |
| 最后编审: | 2020-11-06：zyk |
| 阅读次数: | 36 |