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Semi-supervised Classification and Noise Detection

Authors: Yunna Duan; Ying Gao; Xiaojuan Ren; Haoyuan Che; Keyang Yang;

Semi-supervised Classification and Noise Detection

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 semi-supervised classification results. Experimental results demonstrate the effectiveness of this algorithm.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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