Multi-Label Relational Neighbor Classification using Social Context Features

author: Xi Wang, Department of Electrical Engineering and Computer Science, University of Central Florida
published: Sept. 27, 2013,   recorded: August 2013,   views: 603
Categories

Slides

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

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.

See Also:

Download slides icon Download slides: kdd2013_wang_social_context_01.pdf (1.3┬áMB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: