TY - GEN
T1 - Decision tree ensemble based classification of terrorist attacks using eXplainable Artificial Intelligence
AU - Lamptey, Odartey
AU - Rosner, Anna
AU - Gegov, Alexander
AU - Ouelhadj, Djamila
AU - Hopgood, Adrian Alan
AU - Da Deppo, Serge
PY - 2024/10/9
Y1 - 2024/10/9
N2 - The study proposes using five benchmark machine learning models alongside XGBoost, applied for the first time to an existing case study to predict the success of suicide of terrorist attacks. Utilizing data from the Global Terrorism Database (GTD), the study evaluates model effectiveness to aid decision-making for emergency responders and policymakers. Employing explainable Artificial Intelligence (XAI) models like SHAP ensures transparent decision-making processes. XGBoost performed best for accuracy and performance, while LightGBM excelled in explainability, with SHAP providing global and local insights into their decision-making. The primary goal is to enhance user comprehension and facilitate informed decision-making in critical scenarios, prioritizing transparency, and trustworthiness.
AB - The study proposes using five benchmark machine learning models alongside XGBoost, applied for the first time to an existing case study to predict the success of suicide of terrorist attacks. Utilizing data from the Global Terrorism Database (GTD), the study evaluates model effectiveness to aid decision-making for emergency responders and policymakers. Employing explainable Artificial Intelligence (XAI) models like SHAP ensures transparent decision-making processes. XGBoost performed best for accuracy and performance, while LightGBM excelled in explainability, with SHAP providing global and local insights into their decision-making. The primary goal is to enhance user comprehension and facilitate informed decision-making in critical scenarios, prioritizing transparency, and trustworthiness.
KW - Global Terrorism Database (GTD)
KW - machine learning
KW - Terrorism Prediction
KW - Explainable AI (XAI)
KW - SHAP
UR - https://www.ieee-is.org/important-deadlines/
U2 - 10.1109/IS61756.2024.10705240
DO - 10.1109/IS61756.2024.10705240
M3 - Conference contribution
SN - 9798350350999
T3 - 2024 IEEE 12th International Conference on Intelligent Systems (IS)
BT - Proceedings of IEEE Intelligent Systems IS’24
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE International Conference on Intelligent Systems
Y2 - 29 August 2024 through 31 August 2024
ER -