Terrorist Group Prediction Using Data Classification

Terrorist attacks are the challenging issue across the world and need the attention of the practitioners to cope up deliberately. Predicting the responsible group of an event is a complicated task due to the lack of in depth terrorist historical data. Data mining classification techniques are largely used to resolve the problem. This research proposes a novel ensemble framework for the classification and prediction of the terrorist group that consists of four base classifiers namely; naïve bayes (NB), K nearest neighbour (KNN), Iterative Dichotomiser 3 (ID3) and decision stump (DS). Majority vote based ensemble technique is used to combine these classifiers. The results of individual base classifiers are compared with the majority vote classifier and it is determined through experiments that our approach achieves a considerably better level of accuracy and less classification error rate as compared to the individual classifiers.