An Empirical Study of Supervised and Active Learning Methodologies for Classifying Research Papers

Document classification is one of the predominant tasks in Machine Learning, that by and large involves the use of supervised methodologies to determine most suitable categories for a given set of documents. In this project, we seek to conduct an empirical study, that, in addition to the conventional machine learning methods, experiments with active learning and semi-supervised methods for assorting a mix of labelled and unlabelled computer science scholarly articles hosted on the arXiV repository. We develop a solution using the gathered labelled data points, by experimenting with various multi-class supervised learning models such as K Nearest Neighbours, Naive Bayes, Random Forest, and Logistic Regression. We also account for the issue of class imbalance, and thus conduct experiments to determine a suitable oversampling methodology for addressing this concern. Lastly, we extend beyond the conventional machine learning classification methods to incorporate the concept of active learning, a type of iterative supervised learning, used when the unlabelled data is in abundance.