Multi-label Classification using Random Walk with Restart

摘要：

　　Multi-label classification refers to the task of outputting a label set whose size is unknown for each unseen instance.The challenges of using the random walk method are how to construct the random walk graph and make prediction for testing instances.In this paper,we propose a multi-label classification method based on the random walk with restart model,called ML-RWR.It is derived from the popular KNN algorithm mapping the instances to a KNN based random walk graph.Different from prior work which constructs a complex graph and designs a complex predicting process,we aim at simplifying the complexity of the random walk graph and the complexity of predicting process.Experiments on real-world multi-label datasets show that ML-RWR is superior to those of some well-established multi-label learning algorithm.

关键词： multi-label classification random walk with restart random walk graph

作者: Jinhong Liu Juan Yang

作者单位: Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing,China

会议类型: 国际会议

会议名称: 第九届网络分布式计算与知识发现国际会议( 2017 International Conference on Cyber-enabled distributed computing and knowledge discovery)

会议地点: 南京

会议语种:英文

页码: 206-212

在线出版日期: 2017-10-12（万方平台首次上网日期，不代表论文的发表时间）