Semi-supervised learning with HALFADO: two case studies

Abstract: This thesis studies the HALFADO algorithm[1], a semi-supervised learning al- gorithm designed for detecting anomalies in complex information flows. This report assesses HALFADO’s performance in terms of detection capabilities (pre- cision and recall) and computational requirements. We compare the result of HALFADO with a standard supervised and unsupervised learning approach.The results of two case studies are reported: (1) HALFADO as applied to a FinTech example with a flow of financial transactions, and (2) HALFADO as applied to detecting hate speech in a social media feed. Those results point to the benefits of using HALFADO in environments where one has only modest computational resources.