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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (10): 9-14    DOI: 10.11925/infotech.2096-3467.2018.0708
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Using Bayes Theory to Classify Counter Terrorism Intelligence
Yongnan Li()
School of Criminal Investigation and Counter Terrorism, People’s Public Security University of China, Beijing 100038, China
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[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. [Limitations] 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. [Conclusions] The proposed method, which increases the efficiency of intelligence analysis, is ease of use and has fewer restrictions on the intelligence analysts.

Key wordsBayes Theory      Naive Bayes      Maximum Posteriori Hypothesis      Counter Terrorism Intelligence      Data Mining     
Received: 03 July 2018      Published: 12 November 2018

Cite this article:

Yongnan Li. Using Bayes Theory to Classify Counter Terrorism Intelligence. Data Analysis and Knowledge Discovery, 2018, 2(10): 9-14.

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