Semi-supervised Classification Algorithm Using Fuzzy Nearest Neighborhood Label Propagation

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- Semi-supervised Classification Algorithm Using Fuzzy Nearest Neighborhood Label Propagation
- Vol. 27, Issue 2, Pages: 30-33(2010)
- 作者机构：
  
  中国科学技术大学电子科学与技术系,安徽,合肥,230027
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2010
- 稿件说明：
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郝建柏, 陈贤富, 黄双福, 杨俊. 一种基于模糊近邻标签传递的半监督分类算法[J]. 微电子学与计算机, 2010, 27(2): 30-33.

HAO Jian-bo, CHEN Xian-fu, HUANG Shuang-fu, et al. Semi-supervised Classification Algorithm Using Fuzzy Nearest Neighborhood Label Propagation[J]. 2010, 27(2): 30-33.
郝建柏, 陈贤富, 黄双福, 杨俊. 一种基于模糊近邻标签传递的半监督分类算法[J]. 微电子学与计算机, 2010, 27(2): 30-33. DOI：

HAO Jian-bo, CHEN Xian-fu, HUANG Shuang-fu, et al. Semi-supervised Classification Algorithm Using Fuzzy Nearest Neighborhood Label Propagation[J]. 2010, 27(2): 30-33. DOI：

提出了样本分布无关

模型简单

单控制参数的模糊近邻标签传递算法.该算法依据样本与其k个近邻的模糊相似性连接

使类别标签从标签数据向未标签数据传递

实现未标签数据的分类.最后

通过人工合成数据和UCI数据集中数据的分类实验验证了该算法的简单有效性.

A Fuzzy Nearest Neighborhood Label Propagation algorithm (FNNLP) is proposed

which is sample distribution independent and is simple with only one parameter.Based on the similarity connections of the k nearest data

FNNLP classifies the unlabeled data by making the labels propagate from labeled data to unlabeled ones.Finally

two promising experiment results of synthesized data and UCI dataset classifications verify the simplicity and effectiveness of FNNLP.

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