Indian Journal of Science and Technology
Year: 2018, Volume: 11, Issue: 15, Pages: 1-10
Varun Teja Gundabathula* and V. Vaidhehi
*Author for correspondence
Varun Teja Gundabathula,
Department of Computer Science, Christ University, Bengaluru - 560029, Karnataka, India; [email protected]
Objectives: The study presents best machine learning models that can be employed on terrorism related data to predict the most accurate terrorist group responsible for an attack based on historic data. Methods: This paper analyses terrorism challenges faced in India by modelling the behaviour of terrorist groups using famous machine learning algorithms like J48, IBK, Naive Bayes and ensemble approach using vote. Findings: The results of the evaluation models show the accuracy percentage of various models employed and their relevance to the dataset. It was found that when the classification models are created on a data that has class imbalance problem the percentage of correctly classified instances will be very less. The paper establishes that sampling plays a main role in determining the accuracy percentage of classifier models and gives better results. The study also shows the accuracy percentage of correctly classified instances for various algorithms and shows the ideal one for the dataset. Application/Improvement: The defined model is a new approach to classify data that has major class imbalance problem by using sampling techniques like oversampling, under sampling and ensemble models.
Keywords: Classification, Data Mining, Ensemble, Global Terrorism Database
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