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

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dc.contributor.author Tambouratzis, T en
dc.contributor.author Souliou, D en
dc.contributor.author Chalikias, M en
dc.contributor.author Gregoriades, A en
dc.date.accessioned 2014-03-01T02:46:43Z
dc.date.available 2014-03-01T02:46:43Z
dc.date.issued 2010 en
dc.identifier.uri http://hdl.handle.net/123456789/32806
dc.subject Prediction Accuracy en
dc.subject Probabilistic Neural Network en
dc.subject Relational Data en
dc.subject Decision Tree en
dc.subject.other Accident prediction en
dc.subject.other Accident severity en
dc.subject.other Data sets en
dc.subject.other Input parameter en
dc.subject.other Output parameters en
dc.subject.other Prediction tasks en
dc.subject.other Probabilistic neural networks en
dc.subject.other Recursive construction en
dc.subject.other Accidents en
dc.subject.other Decision trees en
dc.subject.other Forecasting en
dc.subject.other Plant extracts en
dc.subject.other Trees (mathematics) en
dc.subject.other Neural networks en
dc.title Combining probabilistic neural networks and decision trees for maximally accurate and efficient accident prediction en
heal.type conferenceItem en
heal.identifier.primary 10.1109/IJCNN.2010.5596610 en
heal.identifier.secondary 5596610 en
heal.identifier.secondary http://dx.doi.org/10.1109/IJCNN.2010.5596610 en
heal.publicationDate 2010 en
heal.abstract 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 the prediction task or by modifying the records in any way (e.g. via record selection or transformation). Aiming at maximally accurate and efficient prediction, a combination of probabilistic neural networks (PNN's) and decision trees (DT's) is implemented: the simple training and direct operation of the PNN is complemented by the hierarchical, exhaustive and recursive construction of the DT. By training pairs of PNN's on data from the partitions derived from the minimal necessary number of top DT nodes, both efficiency and accident prediction accuracy are maximized. © 2010 IEEE. en
heal.journalName Proceedings of the International Joint Conference on Neural Networks en
dc.identifier.doi 10.1109/IJCNN.2010.5596610 en

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