Active and Multi-View Machine Learning for microRNA Prediction

This thesis explores two emerging trends in semi-supervised machine learning to maximize the utility of both labelled and unlabelled training data. Specifically, this thesis explores the application of active learning and multi-view co-training to microRNA prediction for the first time. Results show that our active learning approach is able to greatly improve classification performance using a small number of labeled instances, outperforming state-of-the-art methods under equivalent training data constraints. Multi-view co-training results also demonstrate improved performance compared to single view classifiers and yield high classification performance using a minimum number of labeled instances for classification. This thesis demonstrates that semi-supervised machine learning is likely to be useful in creating predictors of novel miRNA, particularly for species where few training exemplars are available.