BCCNet: Bayesian classifier combination neural network
This addresses the challenge of processing unstructured data with noisy labels for timely decision-making in developing countries, though it appears incremental as it builds on existing classifier combination methods.
The paper tackled the problem of using imperfect crowdsourced labels for machine learning in developing countries by introducing BCCNet, a framework that aggregates biased labels and trains a classifier, showing efficacy in mosquito sound detection for malaria prevention and damage detection for disaster response.
Machine learning research for developing countries can demonstrate clear sustainable impact by delivering actionable and timely information to in-country government organisations (GOs) and NGOs in response to their critical information requirements. We co-create products with UK and in-country commercial, GO and NGO partners to ensure the machine learning algorithms address appropriate user needs whether for tactical decision making or evidence-based policy decisions. In one particular case, we developed and deployed a novel algorithm, BCCNet, to quickly process large quantities of unstructured data to prevent and respond to natural disasters. Crowdsourcing provides an efficient mechanism to generate labels from unstructured data to prime machine learning algorithms for large scale data analysis. However, these labels are often imperfect with qualities varying among different citizen scientists, which prohibits their direct use with many state-of-the-art machine learning techniques. We describe BCCNet, a framework that simultaneously aggregates biased and contradictory labels from the crowd and trains an automatic classifier to process new data. Our case studies, mosquito sound detection for malaria prevention and damage detection for disaster response, show the efficacy of our method in the challenging context of developing world applications.