Label propagation is one of the most widely used semi-supervised classification methods. It utilizes neighborhood structures of observations to apply the smoothness assumption, which describes that observations close to each other are more likely to share a label. However, a single neighborhood structure cannot appropriately reflect intrinsic data structures, and hence, existing label propagation methods can fail to achieve superior performance. To overcome these limitations, we propose a label propagation algorithm based on consensus rates that are calculated by summarizing multiple clustering solutions to incorporate various properties of the data. Thus, the proposed algorithm can effectively reflect the intrinsic data structures, and yield accurate classification results. Experiments are conducted on various benchmark datasets to examine the properties of the proposed algorithm, and to compare it with the existing label propagation methods. The experimental results confirm that the proposed label propagation algorithm demonstrated superior performance compared to the existing methods.
Bibliographical noteFunding Information:
We thank the editor and referees for their constructive comments and suggestions, which greatly improved the quality of the paper. This work was supported by Brain Korea PLUS, the Basic Science Research Program through the National Research Foundation of Korea , funded by the Ministry of Science, ICT, and Future Planning ( NRF-2016R1A2B1008994 ), and the Ministry of Trade, Industry & Energy under the Industrial Technology Innovation Program ( R1623371 ).
© 2018 Elsevier Inc.
- Consensus rate
- Label propagation
- Semi-supervised classification
- Smoothness assumption
ASJC Scopus subject areas
- Control and Systems Engineering
- Theoretical Computer Science
- Computer Science Applications
- Information Systems and Management
- Artificial Intelligence