摘要
【目的】根据反恐情报的特点对朴素贝叶斯分类器进行修改,为反恐情报数据的分类分析提供一种简单实用的方法。【方法】根据反恐情报的特点删除数据噪声,对相关性较大的属性进行归约,对连续属性进行离散化处理;利用预处理后的样本数据计算不同属性的条件概率;基于最大后验假设判定数据分类。【结果】采用调高概率阈值的方式对最后的分类结果进一步筛选,能部分抵消属性相关性对结果的影响,最后只需对敏感等级较高的数据进行人工情报研判,节约人力成本。【局限】本文方法对数据属性的独立性有一定的要求,在实际使用中需要与决策树等其他分类方法组合使用,才能覆盖更多的情报信息,为反恐预警提供参考。【结论】该方法适用于对属性相关性较小的基础数据进行快速分类,为人工情报研判提供参考依据。
[Objective] This study modifies Naive Bayes Classifier according to the features of counterterrorism intelligence, aiming to provide a simple and practical way to categorize these data. [Methods] Firstly, we deleted the outliers of terrorism related data, discretized continuous attributes, as well as finished reduction of data with high level correlation. Secondly, we computed conditional probabilities of different attributes. Lastly, we classified new sample dataset based on maximum posteriori hypothesis. [Results] After categorizing the data, we raised probability threshold to partially offset the influence of the data dependence. Only some data of high-level sensitivity needs to be process manually. ]LiInitatiousl This method has some restrictions on data independence. In practice, it must be combined with other classification method such as decision tree to cover more intelligence data, and provide information for early warning. [Conclusionsl The proposed method, which increases the efficiency of intelligence analysis, is ease of use and has fewer restrictions on the intelligence analysts.
作者
李勇男
Li Yongnan(School of Criminal Investigation and Counter Terrorism,People＇s Public Security University of China,Beijing 100038,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2018年第10期9-14,共6页
Data Analysis and Knowledge Discovery
基金
教育部人文社会科学研究青年基金项目"基于数据挖掘的涉恐情报量化分析方法研究"(项目编号:17YJCZH098)
北京市社会科学基金项目"大数据驱动的首都反恐情报决策机制研究"(项目编号:18GLC062)
国家社会科学基金重大项目"当前我国反恐形势及对策研究"(项目编号:15ZDA034)的研究成果之一
关键词
贝叶斯理论
朴素贝叶斯
最大后验假设
反恐情报
数据挖掘
Bayes Theory
Naive Bayes
Maximum Posteriori Hypothesis
Counter Terrorism Intelligence
Data Mining
