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BCCNet: Bayesian classifier combination neural network

Isupova, Olga, Li, Yunpeng, Kuzin, Danil, Roberts, Stephen J, Willis, Katherine and Reece, Steven (2018) BCCNet: Bayesian classifier combination neural network In: NeurlPS Workshop on Machine Learning for the Developing World, 2018-12-08, Montreal, Canada.

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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-theart 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.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Computer Science
Authors :
Isupova, Olga
Kuzin, Danil
Roberts, Stephen J
Willis, Katherine
Reece, Steven
Date : 6 November 2018
Copyright Disclaimer : Copyright 2018 The Authors
Related URLs :
Depositing User : Diane Maxfield
Date Deposited : 18 Jul 2019 13:25
Last Modified : 23 Sep 2019 09:04

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