Open AccessJournal Article
Weighted Naive Bayes Classification Algorithm Based on Rough Set
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TL;DR: Methods for determining the weights of attributes in the algebra view, Informational view and both of them are developed respectively, and results on a variety of UCI data sets illustrate the efficiency of this method.
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Abstract: Naive Bayes algorithm is an effective simple classification algorithmSince its conditional independence assumption is not always true in real life,its classification performance is affected to some extentWeighted naive Bayes(simply WNB)is an extension of itBased on the attributes' importance degree theory of rough set,a new weighted naive Bayes method is proposedMethods for determining the weights of attributes in the algebra view,informational view and both of them are developed respectivelySimulation results on a variety of UCI data sets illustrate the efficiency of this method
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Citations
A Novel Bayes Model: Hidden Naive Bayes
TL;DR: This paper summarizes the existing improved algorithms and proposes a novel Bayes model: hidden naive Bayes (HNB), which significantly outperforms NB, SBC, NBTree, TAN, and AODE in terms of CLL and AUC.
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A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier
TL;DR: The Hidden Naive Bayes (HNB) model can be applied to intrusion detection problems that suffer from dimensionality, highly correlated features and high network data stream volumes and significantly improves the accuracy of detecting denial-of-services (DoS) attacks.
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Improving Naive Bayes for Classification
Liangxiao Jiang,Z. Cai,D. Wang +2 more
TL;DR: Experimental results show that IWNB and CNNB all significantly outperform NB, and another two improved algorithms are single out: instance weighted naive Bayes (IWNB) and combined neighbourhood naive Baye (CNNB).
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Weighted Naive Bayes classification algorithm based on particle swarm optimization
Jie Lin,Jiankun Yu +1 more
- 27 May 2011
TL;DR: This paper presents a Weighted Naive Bayes Classification Algorithm Based on PSO (particle swarm optimization, which was first proposed by Kenney and Eberhart[2]).
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A Novel Intrusion Detection System Based on Advanced Naive Bayesian Classification
Yunpeng Wang,Li Yuzhou,Daxin Tian,Wang Congyu,Wenyang Wang,Rong Hui,Peng Guo,Haijun Zhang +7 more
- 21 Apr 2017
TL;DR: A machine learning model, advanced Naive Bayesian Classification (NBC-A) which is based on NBC and ReliefF algorithm, to be used in the novel IDS, which has a higher True Positive rate and a lower False Positive rate in detection performance.
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