Approximating Learning Curves for Imbalanced Big Data with Limited Labels

Abstract

Labeling data for supervised learning can be an expensive task, especially when large amounts of data are required to build an adequate classifier. For most problems, there exists a point of diminishing returns on a learning curve where adding more data only marginally increases model performance. It would be beneficial to approximate this point for scenarios where there is a large amount of data available but only a small amount of labeled data. Then, time and resources can be spent wisely to label the sample that is required for acceptable model performance. In this study, we explore learning curve approximation methods on a big imbalanced dataset from the bioinformatics domain. We evaluate a curve fitting method developed on small data using an inverse power law model, and propose a new semi-supervised method to take advantage of the large amount of unlabeled data. We find that the traditional curve fitting method is not effective for large sample sizes, while the semi-supervised method more accurately identifies the point of diminishing returns.