Partially Generative Neural Networks for Gang Crime Classification with Partial Information

@article{Seo2018PartiallyGN,
  title={Partially Generative Neural Networks for Gang Crime Classification with Partial Information},
  author={Sungyong Seo and H. Chan and P. Brantingham and Jorja Leap and P. Vayanos and Milind Tambe and Y. Liu},
  journal={Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society},
  year={2018}
}
  • Sungyong Seo, H. Chan, +4 authors Y. Liu
  • Published 2018
  • Computer Science
  • Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society
  • More than 1 million homicides, robberies, and aggravated assaults occur in the United States each year. [...] Key Method We introduce a novel Partially Generative Neural Networks (PGNN) that is able to accurately classify gang-related crimes both when full information is available and when there is only partial information. Our PGNN is the first generative-classification model that enables to work when some features of the test examples are missing. Using a crime event dataset from Los Angeles covering 2014…Expand Abstract

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