{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-novel-stacking-framework-based-on-hybrid-of","title":"A novel stacking framework based on hybrid of gradient boosting-adaptive boosting-multilayer perceptron for crash injury severity prediction and analysis","arxiv_id":null,"date":"2021-12-17","proceeding":"2021 IEEE 4th International Conference on Electronics and Communication Engineering (ICECE) 2021 12","authors":["Jovial Niyogisubizo","Lyuchao Liao","Yuyuan Lin","Linsen Luo","Eric Nziyumva","Evariste Murwanashyaka"],"abstract":"Crash injury severity prediction is a promising area of interest in traffic safety and management. Recently, machine learning approaches are becoming popular due to their ability to enhance the prediction performance through the bias-variance trade-off-technique. However, some of these methods are criticized to perform like a ‘black box’ approach while predicting and analyzing crash injury severity and produce low accuracy. In this study, we propose a novel stacking framework based on a hybrid of Gradient Boosting (GB), Adaptive Boosting (AdaBoost), and Multilayer Perceptron (MLP) to predict accurately crash injury severity. On the traffic collision dataset provided by the Seattle City Department of Transportation from 2004 to 2021, the proposed model has demonstrated superior performance when compared with the base models. Furthermore, SHAP (SHapley Additive exPlanation) is used to interpret the contribution of every feature on model performance and provide recommendations to responsible authorities.","url_abs":"https://ieeexplore.ieee.org/abstract/document/9674567","url_pdf":"https://www.researchgate.net/profile/Jovial-Niyogisubizo/publication/357921830_A_Novel_Stacking_Framework_Based_On_Hybrid_of_Gradient_Boosting-Adaptive_Boosting-Multilayer_Perceptron_for_Crash_Injury_Severity_Prediction_and_Analysis/links/63a56a63c3c99660eb99c2b9/A-Novel-Stacking-Framework-Based-On-Hybrid-of-Gradient-Boosting-Adaptive-Boosting-Multilayer-Perceptron-for-Crash-Injury-Severity-Prediction-and-Analysis.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-novel-stacking-framework-based-on-hybrid-of","repo_url":"https://github.com/jovialniyo93/ensemble-machine-learning-for-crash-injury-severity-analysis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"crash-injury-severity","task_name":"Crash injury severity"},{"task_slug":"ensemble-learning","task_name":"Ensemble Learning"},{"task_slug":"feature-importance","task_name":"Feature Importance"},{"task_slug":"management","task_name":"Management"},{"task_slug":"traffic-prediction","task_name":"Traffic Prediction"},{"task_slug":"severity-prediction","task_name":"severity prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}