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- 저자 : Dechang Pi (검색결과 4 건)
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Lin Cui,Dechang Pi,Caiyin Wang 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.5
In recent years, topic detection has become a hot research point of the social network, which can be very good to find the key factors from the massive information and thus discover the topics. The traditional label propagation-based topic discovery algorithm (LPA) is widely concerned because of its approximate linear time complexity and there is no need to define the target function. However, LPA algorithm has the uncertainty and the randomness, which affects the accuracy and the stability of the topic discovery. In this paper, a method for clustering label words based on mutual information analysis is presented to find the current topic. Firstly, through filtering the stop words and extracting keywords with TF-IDF, topic words are been extracted out, and then a common word matrix is built, a topic discovery algorithm based on mutual information and label clustering is put forward. Finally, extensive experiments on two real datasets validate the effectiveness of the proposed MI-LC (Mutual information-Label clustering) algorithm against other well-established methods LPA and LDA in terms of running time, NMI value and perplexity value.
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The Dynamic Influence Graph Model on Mobile Datasets
Zhipeng Liu,Dechang Pi,Yehong Wu 보안공학연구지원센터 2015 International Journal of Database Theory and Appli Vol.8 No.5
With the rapid development of mobile technologies, more and more people are equipped with smartphones. It is possible for scientists to collect and analyze mobile data efficiently. Mobile data contain rich semantic as well as topological information. Rich information can be inferred from these data such as social influence among different nodes in mobile social network. However, it is difficult to estimate the strength of social influence due to the characteristics of inherent dynamic and large scale of mobile social network. In this paper, a Dynamic Influence Graph (DIG) model is proposed which utilizes temporal information in a topological perspective, and an efficient algorithm is proposed based on the DIG model. The proposed algorithm can calculate social influence between any two nodes in a given mobile social network stream segment, and takes edge weights, node connectivity and temporal information into consideration. Experimental results with a real mobile social network dataset show that the proposed approach can infer social influence and achieve a-state-of-the-art accuracy (82-86%) efficiently and automatically.
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A Novel Kernel SVM Algorithm with Game Theory for Network Intrusion Detection
( Yufei Liu ),( Dechang Pi ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.8
Network Intrusion Detection (NID), an important topic in the field of information security, can be viewed as a pattern recognition problem. The existing pattern recognition methods can achieve a good performance when the number of training samples is large enough. However, modern network attacks are diverse and constantly updated, and the training samples have much smaller size. Furthermore, to improve the learning ability of SVM, the research of kernel functions mainly focus on the selection, construction and improvement of kernel functions. Nonetheless, in practice, there are no theories to solve the problem of the construction of kernel functions perfectly. In this paper, we effectively integrate the advantages of the radial basis function kernel and the polynomial kernel on the notion of the game theory and propose a novel kernel SVMalgorithm with game theory for NID, called GTNID-SVM. The basic idea is to exploit the game theory in NID to get a SVM classifier with better learning ability and generalization performance. To the best of our knowledge, GTNID-SVM is the first algorithm that studies ensemble kernel function with game theory in NID. We conduct empirical studies on the DARPA dataset, and the results demonstrate that the proposed approach is feasible and more effective.
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