Author ORCID Identifier
Document Type
Article
Publication Date
11-2-2018
Publication Title
IEEE Access
Volume
6
First Page
70164
Last Page
70171
Abstract
The toll of human casualties and psychological impacts on societies make any study on violent extremism worthwhile, let alone attempting to detect patterns among them. This paper is an effort to predict which violent extremist organization (VEO), among 14 currently active ones throughout the world, is responsible for a violent act based on 14 features, including its human and structural tolls, its target type and value, intelligence, and weapons utilized in the attack. Three main steps in our paper include: 1) the visualization of the violent acts through linear and non-linear dimensionality reduction techniques; 2) sequential forward feature selection based on the generalization accuracy of three machine learning models–decision tree, and linear and nonlinear SVM; and 3) employing multilayer perceptron to predict the VEO based on the selected features of a violent act. Top-ranked selected features were related to the target type and plan and the multilayer perceptron achieved up to 40% test accuracy.
Recommended Citation
Hashemi, Mahdi and Hall, Margeret A., "Visualization, Feature Selection, Machine Learning: Identifying the Responsible Group for Extreme Acts of Violence" (2018). Interdisciplinary Informatics Faculty Publications. 43.
https://digitalcommons.unomaha.edu/interdiscipinformaticsfacpub/43
Creative Commons License
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Included in
Funded by the University of Nebraska at Omaha Open Access Fund
Comments
This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/
Digital Object Identifier 10.1109/ACCESS.2018.2879056