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Method of predicting terrorist attack based on stochastic subspace

A random subspace and prediction method technology, applied in the field of data processing and analysis, can solve problems such as the prediction process is easily disturbed and the prediction accuracy is low

Inactive Publication Date: 2018-02-16
THE 28TH RES INST OF CHINA ELECTRONICS TECH GROUP CORP
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Problems solved by technology

As a result, the forecasting process is easily disturbed, and there is a problem of low forecasting accuracy

Method used

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  • Method of predicting terrorist attack based on stochastic subspace
  • Method of predicting terrorist attack based on stochastic subspace
  • Method of predicting terrorist attack based on stochastic subspace

Examples

Experimental program
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Embodiment 1

[0092] In order to prove the effectiveness of the method for predicting the risk of terrorist attacks based on random subspaces, an experiment is needed. In the simulation, firstly, the terrorist attack risk prediction method based on random subspace and the traditional terrorist attack risk prediction method based on support vector machine algorithm are compared for terrorist attack prediction.

[0093] Proceed as follows:

[0094] Step 1: Establish a training set, with the country as the unit, 2015-2016 as the time interval, apply the sparsity principle of the L1 criterion, and use the online feature extraction algorithm of sparse mapping to extract 10% of the features, that is, 0.1* dimension, and normalize The parameter λ=0.01, the learning efficiency parameter η=0.2, and all parameters are selected in the same way to form a vector.

[0095] Step 2: Count the number of terrorist attacks in this country in the GTD database from February to November 2016, as the Y value.

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Abstract

The invention discloses a method for predicting a terrorist attack based on the stochastic subspace. The method comprises a first step of establishing a training set, and maintaining a linear classifier Wt by an online learner; a second step of counting the number z of terrorist attack events happening in a country in the next month of the month in a GTD database; step 3: randomly selecting s groups of feature subsets from overall features of a given terrorist attack data set using a stochastic subspace method and generating s base classifiers in an integrated classification algorithm of a kernel extreme learning machine; a fourth step of putting the S groups of feature subsets into the kernel extreme learning machine for learning to obtain output results; a fifth step of integrating the outputs of the s base classifiers to obtain a final classification result; and a sixth step of performing model application: inputting the value of an independent variable for each record in a test set to obtain the value of a predictive variable, that is, the probability of a terrorist attack event happening in the next month.

Description

technical field [0001] The invention belongs to the field of data processing and analysis, in particular to a method for predicting terrorist attacks based on random subspaces. Background technique [0002] The social turmoil brought about by terrorist attacks will affect the security of the entire country and cause great harm to social stability. Establishing an accurate terrorist attack risk prediction system can effectively predict the risk of terrorist attack and improve the efficiency of terrorist attack risk management and national security, which has attracted the attention of many national experts and scholars. Because the terrorist attack risk prediction technology has far-reaching significance. Therefore, it has become a key topic of research in the industry and has received extensive attention. Wu Xin of Zhejiang University and others proposed a method for predicting the risk of terrorist attacks based on artificial neural networks. According to the characteris...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/26G06K9/62G06F17/30
CPCG06F16/2462G06Q10/04G06Q50/265G06F18/214G06F18/2451G06F18/254
Inventor 罗子娟吴姗姗葛唯益王羽姜晓夏
Owner THE 28TH RES INST OF CHINA ELECTRONICS TECH GROUP CORP
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