Combining datasets to increase the number of samples and improve model fitting
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. This is useful when the datasets
have a large number of features that are not shared between them. Furthermore,
our framework can also be utilized for data preprocessing by imputing missing
data, i.e., filling in the missing entries while combining different datasets.
To illustrate the power of the proposed methods and their potential usages, we
conduct experiments for various tasks: regression, classification, and for
different data types: tabular data, time series data, when the datasets to be
combined have missing data. We also investigate how the devised methods can be
used with transfer learning to provide even further model training improvement.
Our results indicate that the proposed methods are somewhat similar to transfer
learning in that the merge can significantly improve the accuracy of a
prediction model on smaller datasets. In addition, the methods can boost
performance by a significant margin when combining small datasets together and
can provide extra improvement when being used with transfer learning.