An evaluation of self-training styles for domain adaptation on the task of splice site prediction

We consider the problem of adding a large unlabeled sample from the target domain to boost the performance of a domain adaptation algorithm when only a small set of labeled examples are available from the target domain. In particular, we consider the problem setting motivated by the task of splice site prediction. For this task, annotating a genome using machine learning requires a lot of labeled data, whereas for non-model organisms, there is only some labeled data and lots of unlabeled data. With domain adaptation one can leverage the large amount of data from a related model organism, along with the labeled and unlabeled data from the organism of interest to train a classifier for the latter. Our goal is to analyze the three ways of incorporating the unlabeled data - with soft labels only (i.e., Expectation-Maximization), with hard labels only (i.e., self-training), or with both soft and hard labels - for the splice site prediction in particular, and more broadly for a general iterative domain adaptation setting. We provide empirical results on splice site prediction indicating that using soft labels only can lead to better classifier compared to the other two ways.