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Intelligent recognition algorithm for social network sensitive information based on classification technology

  • * Corresponding author: Weiping Li

    * Corresponding author: Weiping Li 
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  • In the social network, there is the problem of network sensitive information with low accuracy rate of information recognition. To effectively improve the accuracy of intelligent identification of sensitive information, an intelligent recognition algorithm for sensitive information based on improved fuzzy support vector machine is proposed in this paper. The information is collected. The trajectory of the best movement of the information node is found in the low energy cache. In the limited time, the performance of information acquisition is improved by using the mobility of information nodes. According to DFS criterion, the features are added into the feature subset or eliminate the sensitive information. The feature selection algorithm based on multi-label is applied to feature selection of the collected information, so that the information gain between information feature and label set can be used to measure the importance. The improved support vector machine classification algorithm is used to classify the information selected by feature selection, and select effective candidate support vector, reduce the number of training samples, and improve the training speed. The new membership function is defined to enhance the effect of support vector on the construction of fuzzy support vector machine. Finally, the nearest neighbor sample density is applied to the design of membership function to reduce the noise, and achieve intelligent recognition of the sensitive information in the social network. Experimental results show that the accuracy rate of sensitive information intelligent recognition can be effectively improved by using the proposed algorithm.

    Mathematics Subject Classification: 32A20.


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  • Figure 1.  Information node access

    Figure 2.  Preselected effective support vector

    Figure 3.  Membership function based on class center

    Figure 4.  Classification result obtained with the current method

    Figure 5.  The proposed algorithm

    Figure 6.  Comparison of information recognition accuracy rate between different methods

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