Empirical Study of Domain Adaptation with Naïve Bayes on the Task of Splice Site Prediction
Nic Herndon, Doina Caragea · 2014
For many machine learning problems, training an accurate classifier in a supervised setting requires a substantial volume of labeled data. While large volumes of labeled data are currently available for some of these problems, little or no labeled data exists for others. Manually labeling data can be costly and time consuming. An alternative is to learn classifiers in a domain adaptation setting in which existing labeled data can be leveraged from a related problem, referred to as source domain, in conjunction with a small amount of labeled data and large amount of unlabeled data for the problem of interest, or target domain. In this paper, we propose two similar domain adaptation classifiers based on a na¨A±ve Bayes algorithm. We evaluate these classifiers on the difficult task of splice site prediction, essential for gene prediction. Results show that the algorithms correctly classified instances, with highest average area under precision-recall curve (auPRC) values between 18.46% and 78.01%.