Terrorist event prediction based on revealing data

After “9.11” terrorist attacks, more advanced information technologies have been developed to counter terrorism domain to enhance the performance of early warning system. Machine learning based data mining can be applied to predict terrorist event hidden in terrorist attack events and by which the experts expect to get a clear picture of what the terrorists are thinking about in order to step up defense against these organized acts. This paper focuses on the prediction of terrorist event from the Global Terrorism Database (GTD) with data mining techniques. Support Vector Machine (SVM), Naive Bayes (NB) and Logistic Regression (LR) are adopted in this paper. Two feature selection methods including Minimal-redundancy maximal-relevancy (mRMR) and Maximal relevance (Max-Relevance) are used to further improve the classification accuracy. Finally, a detailed comparison of classification performance is presented, where classifier LR with seven optimal feature subset reaches a classification precision of 78.41%, which validates the feasibility of applying machine learning to the terrorism field. We have stressed that the classification methods can be used to map different inherent types in terrorism with both high accuracy and fast speed. Besides, a well-chosen feature selection can lead to the decrement of classification error.