A Comparison among Support Vector Machine and other Machine Learning Classification Algorithms

ABSTRACT Predication of terrorist groups that responsible of terrorist attacks is a challenging task and a promising research area. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. The main objective of this research is to conduct a detailed comparative study among Support Vector Machine as one of the successful prediction classifiers that proved highly performance and other supervised machine learning classification and hybrid classification algorithms. Whereas most promising methods are based on support vector machine (SVM); so there is a need for a comprehensive analysis on prediction accuracy of supervised machine learning algorithms on different experimental conditions, and hence in this research we compare predictive accuracy and comprehensibility of explicit, implicit, and hybrid machine learning models and algorithms. This research based on predicting terrorist groups responsible of attacks in Middle East & North Africa from year 2004 up to 2008 by comparing various standard, ensemble, hybrid, and hybrid ensemble machine learning methods and focusing on SVM. The compared classifiers are categorized into main four types namely; Standard Classifiers, Hybrid Classifiers, Ensemble Classifiers, and Hybrid Ensemble Classifiers. In our study we conduct three different experiments on the used real data, afterwards we compare the obtained results according to four different performance measures. Experiments were carried out using real world data represented by Global terrorism Database (GTD) from National Consortium for the study of terrorism and Responses of Terrorism (START).