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    Predicting seriousness of injury in a traffic accident: a new imbalanced dataset and benchmark

    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.

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    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

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