基于加权样本和共识率的标记传播算法

doi:10.11896/jsjkx.191200103

Computer Science ›› 2021, Vol. 48 ›› Issue (3): 214-219.doi: 10.11896/jsjkx.191200103

• Artificial Intelligence • Previous Articles Next Articles

Label Propagation Algorithm Based on Weighted Samples and Consensus-rate

CHU Jie, ZHANG Zheng-jun, TANG Xin-yao, HUANG Zhen-sheng

School of Science,Nanjing University of Science and Technology,Nanjing 210094,China

Received:2019-12-16 Revised:2020-04-29 Online:2021-03-15 Published:2021-03-05
About author:CHU Jie,born in 1996,postgraduate.His main research interests include data mining and machine learning.
ZHANG Zheng-jun,born in 1965,Ph.D,associate professor.His main research interests include data mining and gra-phic image.
Supported by:
National Statistical Science Research Major Program of China(2018LD01).

Abstract

Abstract: Label Propagation is one of the most widely used semi-supervised classification methods.Consensus rate-based label propagation(CRLP) algorithmconstructs the graph by summarizing multiple clustering solutions to incorporate various properties of the data.Like most graph-based semi-supervised classification method,CRLP focuses on optimizing the graph to improve the performance.In fact,samples are not always evenly distributed.The importance of different samples in the algorithm is diffe-rent.CRLP algorithm is easily affected by the numbers of clustering and the clustering methods,and it is not adaptable to low-dimensional data.To deal with these problems,a label propagation algorithm based on weighted samples and consensus-rate (WSCRLP) is proposed.WSCRLP firstly clusters the dataset multiple times to explore the structure of sample and combines the consensus-rate and the local information of the sample to construct a graph.Secondly,different weights are assigned to labeled samples with different distributions.Finally,semi-supervised classification is performed based on constructed graph and weighted samples.Experiments on real datasets show that the WSCRLP of weighting and constructing graphs on labeled samples can significantly improve classification accuracy,and is superior to other compared methods in 84% of the experiments.Compared with CRLP,WSCRLP not only has better performance,but also is robust to input parameters.

Key words: Consensus-rate, Label propagation, Semi-supervised classification, Weighted samples

CLC Number:

TP301.6

CHU Jie, ZHANG Zheng-jun, TANG Xin-yao, HUANG Zhen-sheng. Label Propagation Algorithm Based on Weighted Samples and Consensus-rate[J].Computer Science, 2021, 48(3): 214-219.

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