【摘要】 传统的恐怖行为预测算法没有考虑到组织会改变其行为策略,而CAPE算法根据组织背景的改变预测行为变化,但其只能根据变化表中存在的背景变化预测行为。为了能根据任意背景变化预测恐怖行为,针对恐怖数据高维小样本的特点,提出了一种利用贝叶斯方法在改进的变化表上预测组织行为的算法。利用贝叶斯方法可快速有效地解决高维小样本分类问题的特性,在改进的变化表上实现对组织行为的预测,从而提高了预测精度和计算效率。此外,考虑到背景的变化会在时间序列上对组织行为产生持续的影响,因此在不同时间滞差下,利用加权的贝叶斯方法预测组织行为。MAROB数据集上多个组织数据的实验结果也表明,所提算法在准确率及时间复杂度上优于CAPE算法。更多还原

【Abstract】 Traditional terrorism behavior prediction algorithms do not consider how the group will change its behaviors.CAPE predicts changes of behaviors according to context variation of organizations,but it only predicts the changes of behavior based on changes of the context,which is existed in its change table.Considering the characteristics of the high dimensions and small samples of terrorism data,this paper proposed a terrorism prediction algorithm based on improved change table using Bayes method,to predict organizational behavior according to any behavior changes.It predicts organization behaviors on the change table due to the fact that Bayes method classifies high dimensions and small sample in a fast and efficient way.Thus,it improves prediction precision and computing efficiency.In addition,considering the continuing effect of the change of the group’s context on its behavior,the weighted Bayes method with different time lags is used to predict the behavior of the organization.Experiments on multiple organization data of MAROB show that,the proposed algorithm is better than CAPE algorithm on accuracy and time complexity.更多还原