A hybrid ensemble learning method for the identification of gang-related arson cases

Abstract Arson is one of the most common crimes, and it has the characteristics of low cost and great harm. In addition to causing casualties and property damage, arson can often have huge social impacts and cause psychological panic in the public. Since arson is more harmful when conducted by a gang, how to effectively identify gang crimes in arson cases has become an important issue. In this paper, we propose a hybrid method that combines ensemble learning and intelligent optimization algorithms to solve this problem. First, we develop the recursive feature elimination (RFE)-based feature selection method to remove redundant features. Second, for the data imbalance problem, we determine the optimal processing algorithm from 18 candidate algorithms. Third, after trying a combination of multiple base classifiers, we obtain the optimal base classifier combination. Fourth, when integrating the prediction results of the base classifier, we propose a weighted ensemble strategy. Finally, we use the differential evolution (DE) algorithm to optimize the parameters of the base classifier and the weight of the combination, which further enhances the identification ability of the model. To verify the actual performance of the proposed method, we conducted experiments on the US National Fire Incident Reporting System (NFIRS) database. The results show that the proposed method is significantly superior to other popular machine learning methods, which proves that this method can provide a more reliable decision basis in the detection of arson cases.