.png){kind=icon}
返回
A robust self-training algorithm based on relative node graph
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-024-06062-0.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Self-training algorithm is a well-known framework of semi-supervised learning. How to select high-confidence samples is the key step for self-training algorithm. If high-confidence examples with incorrect labels are employed to train the classifier, the error will get worse during iterations. To improve the quality of high-confidence samples, a novel data editing technique termed Relative Node Graph Editing (RNGE) is put forward. Say concretely, mass estimation is used to calculate the density and peak of each sample to build a prototype tree to reveal the underlying spatial structure of the data. Then, we define the Relative Node Graph (RNG) for each sample. Finally, the mislabeled samples in the candidate high-confidence sample set are identified by hypothesis test based on RNG. Combined above, we propose a Robust Self-training Algorithm based on Relative Node Graph (STRNG), which uses RNGE to identify mislabeled samples and edit them. The experimental results show that the proposed algorithm can improve the performance of the self-training algorithm.
Keyword:
Semi-supervised learning
High-confidence samples
Self-training
Data editing
期刊
IF:
3.5
论文数:
7.6K
被引数:
1.7W
机构
引用论文
Comparison of haemodynamic responses to dobutamine and salbutamol in cardiogenic shock after acute myocardial infarction..png)
BMJ
OAAI
BMJIF0
OAAI分享收藏How Does Nitrogen and Perenniality Influence Belowground Biomass and Nitrogen Use Efficiency in Small Grain Cereals?Crop Science
Crop ScienceIF0
PREAI
分享收藏A Self-Training Subspace Clustering Algorithm under Low-Rank Representation for Cancer Classification on Gene Expression Data低秩表示下的自训练子空间聚类算法用于基因表达数据的癌症分类OAAI
OAAI分享收藏分享收藏分享收藏
查看更多内容