Big data-based prediction of terrorist attacks. (July 2019)

• Record Type: Journal Article
• Authors: Meng, Xi; Nie, Lingyu; Song, Jiapeng
• Is Part Of: Computers & electrical engineering. Volume 77(2019)
• Page Start: 120
• Page End: 127
• Publication Date: 2019-07
• Subjects: Hybrid classifier -- Classification -- Prediction -- Genetic algorithm -- Optimisation; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2019.05.013
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: An optimised hybrid classifier is proposed for the prediction of terrorist attacks. Hybrid classifier is designed using big data. It puts forward a framework that includes data collection, preprocessing, hybrid classification mining, and classifier testing as a single unit in predicting the terrorist attacks. The genetic algorithm is used to optimise the weight of each single classifier to improve the prediction accuracy of the hybrid classifier. The results reveal that the hybrid classifier is superior to the single classifier in prediction accuracy.