Combining datasets to increase the number of samples and improve model fitting

Understand

For many use cases, combining information from different datasets can be of interest to improve a machine learning model's performance, especially when the number of samples from at least one of the datasets is small.

• However, a potential challenge in such cases is that the features from these datasets are not identical, even though there are some commonly shared features among the datasets.
• To tackle this challenge, we propose a novel framework called Combine datasets based on Imputation (ComImp).
• In addition, we propose a variant of ComImp that uses Principle Component Analysis (PCA), PCA-ComImp in order to reduce dimension before combining datasets.