Decision tree ensemble based classification of  terrorist attacks using eXplainable Artificial Intelligence

Odartey Lamptey; Anna Rosner; Alexander Gegov; Djamila Ouelhadj; Adrian Alan Hopgood; Serge Da Deppo

doi:10.1109/IS61756.2024.10705240

Decision tree ensemble based classification of terrorist attacks using eXplainable Artificial Intelligence

Odartey Lamptey, Anna Rosner, Alexander Gegov, Djamila Ouelhadj, Adrian Alan Hopgood, Serge Da Deppo

NATO Defense College

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

104 Downloads (Pure)

Abstract

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.

Original language	English
Title of host publication	Proceedings of IEEE Intelligent Systems IS’24
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350350982
ISBN (Print)	9798350350999
DOIs	https://doi.org/10.1109/IS61756.2024.10705240
Publication status	Published - 9 Oct 2024
Event	12th IEEE International Conference on Intelligent Systems - Varna, Bulgaria Duration: 29 Aug 2024 → 31 Aug 2024

Publication series

Name	2024 IEEE 12th International Conference on Intelligent Systems (IS)
Publisher	IEEE
ISSN (Print)	2832-4145
ISSN (Electronic)	2767-9802

Conference

Conference	12th IEEE International Conference on Intelligent Systems
Country/Territory	Bulgaria
City	Varna
Period	29/08/24 → 31/08/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Global Terrorism Database (GTD)
machine learning
Terrorism Prediction
Explainable AI (XAI)
SHAP

Access to Document

10.1109/IS61756.2024.10705240

Decision tree ensemble based classification of terrorist attacks
© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript (Post-print), 377 KB

Cite this

Lamptey, O., Rosner, A., Gegov, A., Ouelhadj, D., Hopgood, A. A., & Da Deppo, S. (2024). Decision tree ensemble based classification of terrorist attacks using eXplainable Artificial Intelligence. In Proceedings of IEEE Intelligent Systems IS’24 (2024 IEEE 12th International Conference on Intelligent Systems (IS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IS61756.2024.10705240

@inproceedings{da2b4a701fca4f589300c5ed69bb22be,

title = "Decision tree ensemble based classification of terrorist attacks using eXplainable Artificial Intelligence",

abstract = "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.",

keywords = "Global Terrorism Database (GTD), machine learning, Terrorism Prediction, Explainable AI (XAI), SHAP",

author = "Odartey Lamptey and Anna Rosner and Alexander Gegov and Djamila Ouelhadj and Hopgood, \{Adrian Alan\} and \{Da Deppo\}, Serge",

year = "2024",

month = oct,

day = "9",

doi = "10.1109/IS61756.2024.10705240",

language = "English",

isbn = "9798350350999",

series = "2024 IEEE 12th International Conference on Intelligent Systems (IS)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "Proceedings of IEEE Intelligent Systems IS{\textquoteright}24",

address = "United States",

note = "12th IEEE International Conference on Intelligent Systems ; Conference date: 29-08-2024 Through 31-08-2024",

}

Lamptey, O , Rosner, A , Gegov, A , Ouelhadj, D , Hopgood, AA & Da Deppo, S 2024, Decision tree ensemble based classification of terrorist attacks using eXplainable Artificial Intelligence. in Proceedings of IEEE Intelligent Systems IS’24. 2024 IEEE 12th International Conference on Intelligent Systems (IS), Institute of Electrical and Electronics Engineers Inc., 12th IEEE International Conference on Intelligent Systems, Varna, Bulgaria, 29/08/24. https://doi.org/10.1109/IS61756.2024.10705240

Decision tree ensemble based classification of terrorist attacks using eXplainable Artificial Intelligence. / Lamptey, Odartey ; Rosner, Anna ; Gegov, Alexander et al.
Proceedings of IEEE Intelligent Systems IS’24. Institute of Electrical and Electronics Engineers Inc., 2024. (2024 IEEE 12th International Conference on Intelligent Systems (IS)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 -

Lamptey O , Rosner A , Gegov A , Ouelhadj D , Hopgood AA, Da Deppo S. Decision tree ensemble based classification of terrorist attacks using eXplainable Artificial Intelligence. In Proceedings of IEEE Intelligent Systems IS’24. Institute of Electrical and Electronics Engineers Inc. 2024. (2024 IEEE 12th International Conference on Intelligent Systems (IS)). doi: 10.1109/IS61756.2024.10705240