Please use this identifier to cite or link to this item:
http://hdl.handle.net/10174/33846
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| Title: | Road Accident Predictions as a Classification Problem |
| Authors: | Agrawal, Madhulika
Gonçalves, Teresa
Quaresma, Paulo |
| Issue Date: | 2021 |
| Citation: | Madhulika Agrawal, Teresa Gonçalves, and Paulo Quaresma. Road Accident Predictions
as a Classification Problem. In Proceedings of the 27th Portuguese Conference on Pattern
Recognition, RECPAD 2021, 2021. |
| Abstract: | This paper aims at evaluating the performance of various classification
methods for road accident prediction. The data is collected under MO-
PREVIS [3] project which aims at improving road safety in Portugal. The
data is highly imbalanced as there are fewer accident instances than the
non-accident ones and due to this imbalance, it is observed that the tra-
ditional classification algorithms do not perform well. Using sampling
techniques (undersampling and oversampling) improved the results but
not significantly. Some methods resulted in increased recall but that de-
creased precision as the algorithm returned more false positives to make
up for data imbalance. |
| URI: | http://hdl.handle.net/10174/33846 |
| Type: | article |
| Appears in Collections: | INF - Artigos em Livros de Actas/Proceedings
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