基于特征选择及机器学习的犯罪预测方法综述

基于警务数据和时空数据构建犯罪预测模型,利用机器学习手段进行案事件预测,在国家安全稳定领域具有重要的意义。犯罪预测涉及三个主要方面:特征选择与处理、预测模型和地理信息可视化。分析了犯罪预测理论与方法的基本思想,在探索犯罪的生成机理和演化规律基础上,对经验模型和时空模型研究成果进行了综述。在此基础上,对根据不同预测特征选取最优算法的策略进行了讨论,同时对比简述了各类算法的特点,并对现存问题和未来研究方向进行了探讨。

A Review of crim prediction methods based on feature selection and machine learning

The crime prediction model is needed for national security and stability. It is based on police affairs data and temporal-spatial data, predicting cases and events through machine learning. Three aspects are mainly involved in crime prediction: feature selection, prediction method and geographic information visualization. In this paper, the basic ideas of the theory and method of crime predictions are introduced. Besides, based on the generation and evolution of crime, the research results of empirical models and temporal-spatial models are summarized. Then, the strategy of selecting the optimal algorithm is analyzed by different predicted characteristics. Meanwhile, the characteristics of various crime prediction algorithms are compared. In the final part, existing problems and future research directions are analyzed.