Investigating Transfer Learners for Robustness to Domain Class Imbalance
Karl R. Weiss, Taghi M. Khoshgoftaar · 2016
A transfer learning environment is characterized by a machine learning algorithm being trained with data from one domain (the source domain) and being tested on data from a different domain (the target domain). In a transfer learning scenario, the class probability of the source domain may be different from the class probability of the target domain, which is referred to as "domain class imbalance". Domain class imbalance is different from "class imbalance". Class imbalance refers to the condition of a single domain having unequal class probabilities. In traditional machine learning, the training and testing data are drawn from a single domain. The effects of class imbalance in traditional machine learning are well studied, however, the issue of domain class imbalance in the field of transfer learning has received little research attention. This paper provides a comparative performance test of state-of-the-art transfer learning algorithms, using a wide-range of domain class imbalance combinations. A detailed discussion on the relative performances of the different algorithms, with statistical validation, is presented for the different domain class imbalance scenarios.