A Study of the Impact of Base Traditional Learners on Transfer Learning Algorithms

A transfer learning environment is characterized by not having sufficient labeled training data from the domain of interest (target domain) to build a high-performing machine learner. Transfer learning algorithms use labeled data from an alternate domain (source domain), that is similar to the target domain, to build high-performing learners. The design of a transfer learning algorithm is typically comprised of a domain adaptation step following by a learning step. The domain adaptation step attempts to align the distribution differences between the source domain and the target domain. Then, the aligned data from the domain adaptation step is used in the learning step, which is typically implemented with a traditional machine learning algorithm. Our research studies the impact of the learning step on the performance of various transfer learning algorithms. In our experiment, we use five unique domain adaptation methods coupled with seven different traditional machine learning methods to create 35 different transfer learning algorithms. We perform comparative performance analyses of the 35 transfer learning algorithms, along with the seven stand-alone traditional machine learning methods. This research will aid machine learning practitioners in the algorithm selection process for a transfer learning environment in the absence of reliable validation techniques.