Prediction of Unknown Terrorist Group Names Responsible for Attacks in Turkey

Abstract

In this paper, the dataset of real incidents that occurred in Turkey between 2013 and 2017 and are regarded as acts of terrorism without any doubt according to Global Terrorism Database (GTD) are used to predict the group names responsible for unknown attacks. Principal Component Analysis (PCA) technique was used for feature selection. A novel voting method between five classification algorithms such as Random Forests, Logistic Regression, AdaBoost, Neural Network, and Support Vector Machine was used to predict the names. The results clearly demonstrate that the classification accuracy of all classifiers studied in this paper improved when PCA was used to select features as compared to selecting features without using PCA. The prediction of terrorist group names with PCA based feature reduction and the original features is carried out and the results are compared.

Keywords

• Prediction
• Classification
• GTD dataset
• PCA

References

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