Abstract—Terrorist attacks are the biggest challenging
problem for the mankind across the world, which need the
wholly attention of the researchers, practitioners to cope up
deliberately. To predict the terrorist group which is responsible
of attacks and activities using historical data is a complicated
task due to the lake of detailed terrorist data. This research
based on predicting terrorist groups responsible of attacks in
Egypt from year 1970 up to 2013 by using data mining
classification technique to compare five base classifiers namely;
Naïve Bayes (NB), K-Nearest Neighbour (KNN), Tree Induction
(C4.5), Iterative Dichotomiser (ID3), and Support Vector
Machine (SVM) depend on real data represented by Global
terrorism Database (GTD) from National Consortium for the
study of terrorism and Responses of Terrorism (START). The
goal of this research is to present two different approaches to
handle the missing data as well as provide a detailed
comparative study of the used classification algorithms and
evaluate the obtained results via two different test options.
Experiments are performed on real-life data with the help of
WEKA and the final evaluation and conclusion based on four
performance measures which showed that SVM, is more
accurate than NB and KNN in mode imputation approach, ID3
has the lowest classification accuracy although it performs well
in other measures, and in Litwise deletion approach; KNN
outperformed the other classifiers in its accuracy, but the overall
performance of SVM is acceptable than other classifiers.
Index Terms—KDD, precision, recall, terrorist group.
The authors are with Operations Research Department, Cairo University,
Egypt (e-mail: gh.tolan@fci-cu.edu.eg, O.Soliman@fci-cu.edu.eg).
[PDF]
Cite: Ghada M. Tolan and Omar S. Soliman, "An Experimental Study of Classification Algorithms for Terrorism Prediction," International Journal of Knowledge Engineering vol. 1, no. 2, pp. 107-112, 2015.
