Support Vector Machine for Classification of Terrorist Attacks Based on Intelligent Tuned Harmony Search

Bing Bu, Zhenyang Pi, Lei Wang

Ekoloji, 2019, Issue 107, Pages: 153-164, Article No: e107040


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The classification of terrorist attacks plays an important role in ensuring optimal emergency resource allocation and effective implementation for contingency plan. This paper integrates support vector machine (SVM) with intelligent tuned harmony search (ITHS) to develop ITHS-SVM model for the classification of terrorist attacks, in which SVM provides learning and curve fitting functions while ITHS optimizes parameters of SVM. Through an experiment, the superiority of ITHS-SVM classification model on terrorist attack is verified with sample data of terrorist attacks in China during 2009-2016. Compared with six other classification models, ITHS-SVM exhibits the best performance on all four evaluation metrics of accuracy, precision, sensitivity and empirical error rate (EER). Our study demonstrates that ITHS-SVM model is effective for classification of terrorist attacks and can provide scientific basis for decision makers in response to terrorist attacks.


terrorist attack, classification, support vector machine (SVM), intelligent tuned harmony search (ITHS)


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