TITLE: Predicting Seriousness of Injury in a Traffic Accident: A New Imbalanced Dataset and Benchmark 

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.
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.
Predicting Seriousness of Injury in a Traffic Accident: A New Imbalanced Dataset and Benchmark
http://arxiv.org/abs/2205.10441v1