Abstract: In machine learning studies, feature selection presents a crucial step especially when handling complex and imbalanced datasets, such as those used in road traffic injury analysis. This study proposes a novel multitasking feature selection methodology that integrates the Grey Wolf Optimizer, knowledge transfer, and the CatBoost ensemble algorithm to enhance the performance and interpretability of road accident severity prediction. The main objective of this study is to identify critical features impacting the prediction of severe injury cases in road accidents. The proposed framework integrates several steps to handle the complexities related to feature selection. The fitness function of the Grey Wolf Optimizer model is designed to prioritize the classification accuracy of the severe injury class. To mitigate early convergence of the model, a knowledge transfer mechanism that generates new wolf instances based on a historical record of wolves used previously is integrated within a multitasking process. To evaluate the prediction performance of the generated feature subsets, the CatBoost algorithm is employed in the evaluation step to assess the effectiveness of the proposed approach. By Integrating these three step methodology which combine metaheuristic feature selection technique with knowledge transfer through a multitasking process, the proposed framework enhances generalization, reduces prediction models complexity and handles imbalanced distributions. It proposed a feature selection model that overcomes key limitations of traditional methods. Applied to real-world road crash data, the methodology significantly improves the identification of factors impacting the severity of injuries. Experimental results demonstrate enhanced model performance, reduced complexity, and deeper insights into the factors contributing to traffic injuries. These findings highlight the potential of advanced machine learning techniques in improving road safety analysis and supporting data-driven decision-making.

Soumaya AMRI, Mohammed AL ACHHAB and Mohamed LAZAAR, “A Novel Multitasking Framework for Feature Selection in Road Accident Severity Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 16(4), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160429

@article{AMRI2025,
title = {A Novel Multitasking Framework for Feature Selection in Road Accident Severity Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160429},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160429},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {4},
author = {Soumaya AMRI and Mohammed AL ACHHAB and Mohamed LAZAAR}
}