Digital archive label unbalanced sample classification algorithm based on multi-feature fusion
Due to the presence of redundant information in digital archive label samples,the number of samples in different categories is imbalanced,and the completeness of data information cannot be guaranteed during the classification process,resulting in inaccurate classification.To this end,a digital archive label unbalanced sample classification algorithm based on multi feature fusion is proposed.Using a multi feature fusion model,calculate similarity features,derive standard fusion conditions for data samples,solve dimensionality reduction parameters,and complete dimensionality reduction processing for digital archive labels.Construct the relationship features of imbalanced samples and combine them with iterative classification standards to determine the classification of imbalanced samples in digital archive labels.The experimental results show that the application of the proposed algorithm can transmit digital archive information to the correct target location,meeting the requirements of precise classification applications.
万方数据
维普
