Transfer Learning by Fuzzy Neighborhood Density-Based Clustering and Re-sampling

In many machine learning algorithms, a major assumption is that the training samples and the test samples have the same distribution. However, this assumption does not hold in many real applications. In recent years, transfer learning has attracted a significant amount of attention to solve this problem. Among these methods, an effective algorithm based on clustering analysis and re-sampling can correct different types of domain differences and does not need to estimate the different distribution directly. As the critical part, its original clustering method is not good enough at data structure exploration due to poor robustness on the data with various shapes and densities. In this paper, a new transfer learning algorithm based on fuzzy neighborhood density-based clustering and re-sampling is proposed, which is more robust to datasets with various shapes and densities, and could explore more data structure information. With the better explored data structure information, the proposed method can transfer more useful knowledge from source domain to target domain. Validation of the proposed method is performed with extensive experiments.