• Title/Summary/Keyword: 트리확장 순수 베이지안 네트워크

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Bayesian Network-based Data Analysis for Diagnosing Retinal Disease (망막 질환 진단을 위한 베이지안 네트워크에 기초한 데이터 분석)

  • Kim, Hyun-Mi;Jung, Sung-Hwan
    • Journal of Korea Multimedia Society
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    • v.16 no.3
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    • pp.269-280
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    • 2013
  • In this paper, we suggested the possibility of using an efficient classifier for the dependency analysis of retinal disease. First, we analyzed the classification performance and the prediction accuracy of GBN (General Bayesian Network), GBN with reduced features by Markov Blanket and TAN (Tree-Augmented Naive Bayesian Network) among the various bayesian networks. And then, for the first time, we applied TAN showing high performance to the dependency analysis of the clinical data of retinal disease. As a result of this analysis, it showed applicability in the diagnosis and the prediction of prognosis of retinal disease.

IDS Model using Improved Bayesian Network to improve the Intrusion Detection Rate (베이지안 네트워크 개선을 통한 탐지율 향상의 IDS 모델)

  • Choi, Bomin;Lee, Jungsik;Han, Myung-Mook
    • Journal of the Korean Institute of Intelligent Systems
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    • v.24 no.5
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    • pp.495-503
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    • 2014
  • In recent days, a study of the intrusion detection system collecting and analyzing network data, packet or logs, has been actively performed to response the network threats in computer security fields. In particular, Bayesian network has advantage of the inference functionality which can infer with only some of provided data, so studies of the intrusion system based on Bayesian network have been conducted in the prior. However, there were some limitations to calculate high detection performance because it didn't consider the problems as like complexity of the relation among network packets or continuos input data processing. Therefore, in this paper we proposed two methodologies based on K-menas clustering to improve detection rate by reforming the problems of prior models. At first, it can be improved by sophisticatedly setting interval range of nodes based on K-means clustering. And for the second, it can be improved by calculating robust CPT through applying weighted-leaning based on K-means clustering, too. We conducted the experiments to prove performance of our proposed methodologies by comparing K_WTAN_EM applied to proposed two methodologies with prior models. As the results of experiment, the detection rate of proposed model is higher about 7.78% than existing NBN(Naive Bayesian Network) IDS model, and is higher about 5.24% than TAN(Tree Augmented Bayesian Network) IDS mode and then we could prove excellence our proposing ideas.