Combining probabilistic neural networks and decision trees for maximally accurate and efficient accident prediction

@article{Tambouratzis2010CombiningPN,
  title={Combining probabilistic neural networks and decision trees for maximally accurate and efficient accident prediction},
  author={Tatiana Tambouratzis and Dora Souliou and Miltiadis S. Chalikias and Andreas Gregoriades},
  journal={The 2010 International Joint Conference on Neural Networks (IJCNN)},
  year={2010},
  pages={1-8}
}
The extent to which accident severity can be predicted from accident-related data collected at a variety of locations is investigated. The 2005 accident dataset brought together by the Republic of Cyprus Police is employed; this dataset comprises 1407 records of 43 continuous and categorical input parameters and a single categorical output parameter representing accident severity. No transformation of the database has been opted for, either by extracting the parameters that are significant for… CONTINUE READING

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