基于动态域定界的循环分类模型

A Cyclic Classification Model Based on Dynamic Domain Delimitation

在多源域迁移学习应用中,现有的分类模型大多对数据标签要求较高,难以适应数据集的动态变化和标签缺失情况.针对该问题,提出一种基于动态域定界的循环分类模型CAMDOT(cyclic classification model based on dynamic domain delimitation).首先,引入互信息量化各数据域间的相关性,提高分类模型适应异构用户和数据动态变化的能力.其次,针对数据标签缺失和不平衡问题,提出一种循环分类算法.最后,通过理论与实验分析,验证该模型在多源域数据训练中具有较高的分类准确率.

In the application of multi-source domain transfer learning, most of the existing classification models have high requirements for data labels, which is difficult to adapt to the dynamic changes of datasets and the absence of labels. To address this problem, CAMDOT(cyclic classification model based on dynamic domain delimitation) is proposed. Firstly, mutual information is introduced to quantify the correlation between data domains and improve the ability of the classification model to adapt to heterogeneous users and dynamic changes of data. Secondly, a cyclic classification algorithm is proposed for the problem of missing and unbalanced data labels. Finally, through theoretical and experimental analysis, it is verified that the model in this paper has high classification accuracy in multi-source domain data training.