Optimizing Parameters of Support Vector Machine Using Harmony Search Algorithm for Emergency Classification of Terrorist Attacks

Abstract

Abstract:

The classification on terrorist attacks plays an important role to ensure optimal allocation of emergency resources and reasonable implementation for emergency plan. This study proposes a model to integrate a harmony search with a support vector machine (SVM) to research classification of terrorist attacks. The support vector machine provides learning and curve fitting while harmony search optimizes support vector machine parameters. Measures in term of accuracy, precision and sensitivity are used for performance evaluation of proposed hybrid intelligence classification model. The data of global terrorism database from 2008 to 2013 in china is used for testing, and experimental comparisons indicate that HS-based SVM achieves better accuracy compared to SVM, CART and C5.0. Experimental results show that HSSVM is a feasible approach dealing with emergency classification problems, and provides warning and decision support information needed to manage emergency terrorist attacks.

Key words: terrorist attacks, emergency classification, harmony search, support vector machine

Wang Lei, Wang Xin, Zhao Qiuhong. Optimizing Parameters of Support Vector Machine Using Harmony Search Algorithm for Emergency Classification of Terrorist Attacks[J]. , 2016, 28(8): 125-132.

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