Comparing Transfer Learning and Traditional Learning Under Domain Class Imbalance

Abstract

Transfer learning is a subclass of machine learning, which uses training data (source) drawn from a different domain than that of the testing data (target). A transfer learning environment is characterized by the unavailability of labeled data from the target domain, due to data being rare or too expensive to obtain. However, there exists abundant labeled data from a different, but similar domain. These two domains are likely to have different distribution characteristics. Transfer learning algorithms attempt to align the distribution characteristics of the source and target domains to create high-performance classifiers. This paper provides comparative performance analysis between stateof- the-art transfer learning algorithms and traditional machine learning algorithms under the domain class imbalance condition. The domain class imbalance condition is characterized by the source and target domains having different class probabilities, which can create marginal distribution differences between the source and target data. Statistical analysis is provided to show the significance of the results.