关键词: 线性邻近点传播, 半监督, 类重叠, 分布不平衡

Abstract: Linear Neighborhood Propagation（LNP） is a very effectively graph-based semi-supervised classification method. However, different class overlapping and data distributed imbalance cause the choice of the neighbors to be unreasonable when constructing the graph in LNP. This paper applies spectral clustering to analyzing the data’s distribution, adjusts the distance metric in the choice of neighbors to make the neighbors more reasonable according to the clustering result. LNP method based on spectral clustering is applied to the time series classification. Using four time series datasets from UCR time series data mining archive, the experimental results show that spectral clustering based LNP acquires higher accuracy than LNP.

Key words: Linear Neighborhood Propagation（LNP）, semi-supervised, class overlapping, imbalance