A Multi-Label Threshold Learning Framework for Propagation Algorithms on a Non-Feature Network
Ye-Yan Zeng, Bi-Ru Dai · 2018
Recently, with the exponential growth of network data, collecting whole features correctly is time- consuming and expensive. For a classification problem on networks, traditional propagation algorithms, which rely on the feature information to build transition matrix to propagate label information on networks, generally do not perform well when the feature information is not available. Our observation shows that the problem of minority ignorance occurs on the propagation process of traditional algorithms. In this paper, we propose a LPBC framework to allow a propagation algorithm to deal with multi-label classification problem on networks. With a novel threshold training process, LPBC reduces the minority ignorance when the label information is propagated. Experimental results demonstrated the effectiveness and the performance improvement of the proposed framework.