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Author (up) Hafiz Budi Firmansyah; Jesus Cerquides; Jose Luis Fernandez-Marquez pdf  isbn
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  Title Ensemble Learning for the Classification of Social Media Data in Disaster Response Type Conference Article
  Year 2022 Publication ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2022  
  Volume Issue Pages 710-718  
  Keywords Ensemble learning; image classification; social media; disaster response  
  Abstract Social media generates large amounts of almost real-time data which has proven valuable in disaster response. Specially for providing information within the first 48 hours after a disaster occurs. However, this potential is poorly exploited in operational environments due to the challenges of curating social media data. This work builds on top of the latest research on automatic classification of social media content, proposing the use of ensemble learning to help in the classification of social media images for disaster response. Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Experimental results show that ensemble learning is a valuable technology for the analysis of social media images for disaster response,and could potentially ease the integration of social media data within an operational environment.  
  Address Citizen Cyberlab, CUI, University of Geneva, Switzerland; Citizen Cyberlab, CUI, University of Geneva, Switzerland; IIIA-CSIC, Barcelona, Spain  
  Corporate Author Thesis  
  Publisher Place of Publication Tarbes, France Editor Rob Grace; Hossein Baharmand  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 978-82-8427-099-9 Medium  
  Track Social Media for Crisis Management Expedition Conference  
  Notes Approved no  
  Call Number ISCRAM @ idladmin @ Serial 2450  
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