基于机器学习的恐怖分子预测算法

当今世界恐怖袭击事件频繁发生,通过对嫌疑人进行预测分析,有利于尽早发现新生或者隐藏的恐怖分子并对其进行针对性打击,以减少人员和财产损失。为此,使用机器学习方法,提取恐怖袭击事件的多方面特征,对一个或多个嫌疑人进行预测。采用贝叶斯优化对Bagging、决策树、随机森林和全连接神经网络4种算法进行寻优,将预处理后的数据输入优化后的算法模型对恐怖袭击事件嫌疑人进行预测,以准确率、召回率、精度和F 1值作为指标评价算法性能。实验结果表明,当预测结果仅输出一个嫌疑人时,基于树的算法预测结果普遍较好,其中Bagging算法的预测精度最高为0.911,而全连接神经网络可以得到多个嫌疑人的预测结果,其预测精度为0.8778。

Terrorist Prediction Algorithm Based on Machine Learning

Terrorist attacks happen frequently in the world today.Predicting and analyzing the suspects is beneficial to find new or hidden terrorists as early as possible and launch a targeted operation against them,so as to reduce the loss of people and property.Therefore,machine learning methods are used to predict one or more suspects based on the multiple characteristics of terrorist attacks.Bayesian optimization is used to optimize four algorithms,including Bagging,decision tree,random forest and Fully Connected Neural Network(FCNN).Then,the preprocessed data is input into an optimized algorithm model to predict the suspects of terrorist attacks.The accuracy,recall,precision and F 1 values are used as indicators to evaluate the performance of the algorithm.Experimental results show that,when the prediction result only outputs one suspect,the prediction result of the algorithm based on tree structure is generally good,in which,the prediction accuracy of the Bagging algorithm is 0.911 at the highest,while the FCNN can obtain the prediction results of multiple suspects,with a prediction accuracy of 0.8778.