Multi-tree genetic programming for feature construction-based domain adaptation in symbolic regression with incomplete data

Nowadays, transfer learning has gained a rapid popularity in tasks with limited data available. While traditional learning limits the learning process to knowledge available in a specific (target) domain, transfer learning can use parts of knowledge extracted from learning in a different (source) domain to help learning in the target domain. This concept is of special importance when there is a lack of knowledge in the target domain. Consequently, since data incompleteness is a serious cause of knowledge shortage in real-world learning tasks, it can be typically addressed using transfer learning. One way to achieve that is feature construction-based domain adaptation. However, although it is considered as a powerful feature construction algorithm, Genetic Programming has not been fully utilized for domain adaptation. In this work, a multi-tree genetic programming method is proposed for feature construction-based domain adaptation. The main idea is to construct a transformation from the source feature space to the target feature space, which maps the source domain close to the target domain. This method is utilized for symbolic regression with missing values. The experimental work shows encouraging potential of the proposed approach when applied to real-world tasks considering different transfer learning scenarios.