Semi-supervised classification and noise detection

Authors:
Yunna Duan;Ying Gao;Xiaojuan Ren;Haoyuan Che;Keyang Yang
Affiliations:
Computer Department, Jilin University, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China;Computer Department, Jilin University, Changchun, China;Computer Department, Jilin University, Changchun, China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Year:
2009

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Abstract

Semi-supervised learning has become a topic of significant interests recently. In this paper, we are concerned with semi-supervised classification and noise detection. Based on label propagation algorithm, we present an improved label propagation algorithm, which can classify data and detect noise simultaneously. Compared with original label propagation algorithm, by detecting noise and constraining some labels that can be propagated, the improved algorithm can prevent propagating mislabels and avoid results' tendency to the larger number of labels, so as to improve the semisupervised classification results. Experimental results demonstrate the effectiveness of this algorithm.