Lagias, Paschalis and Magoulas, George and Prifti, Ylli and Provetti, Alessandro (2022) Predicting seriousness of injury in a traffic accident: a new imbalanced dataset and benchmark. Communications in Computer and Information Science 1600 , pp. 412-423. ISSN 1865-0937.
|
Text
lagias-predicting-EANN22.pdf - Author's Accepted Manuscript Download (138kB) | Preview |
Abstract
The paper introduces a new dataset to assess the performance of machine learning algorithms in the prediction of the seriousness of injury in a traffic accident. The dataset is created by aggregating publicly available datasets from the UK Department for Transport, which are drastically imbalanced with missing attributes sometimes approaching 50% of the overall data dimensionality. The paper presents the data analysis pipeline starting from the publicly available data of road traffic accidents and ending with predictors of possible injuries and their degree of severity. It addresses the huge incompleteness of public data with a MissForest model. The paper also introduces two baseline approaches to create injury predictors: a supervised artificial neural network and a reinforcement learning model. The dataset can potentially stimulate diverse aspects of machine learning research on imbalanced datasets and the two approaches can be used as baseline references when researchers test more advanced learning algorithms in this area.
Metadata
| Item Type: | Article |
|---|---|
| Additional Information: | Engineering Applications of Neural Networks. 23rd International Conference, EAAAI/EANN 2022 Chersonissos, Crete, Greece, June 17–20, 2022. ISBN: 9783031082221 |
| Keyword(s) / Subject(s): | Class imbalance, Data imputation, Feature engineering, Neural networks, Reinforcement learning, Q–learning, Traffic accidents |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Alessandro Provetti |
| Date Deposited: | 30 Nov 2022 06:06 |
| Last Modified: | 09 Aug 2023 12:54 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/49970 |
Statistics
Additional statistics are available via IRStats2.
Archive Staff Only (login required)
