A Naïve Bayesian network approach to determine the potential drivers of the toxic dinoflagellate Coolia monotis (Meunier, 1919) in the Gulf of Gabès, Tunisia

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Wafa Feki-Sahnoun ◽  
Hasna Njah ◽  
Moufida Abdennadher ◽  
Asma Hamza ◽  
Nouha Barraj ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Network Approach ◽  
Toxic Dinoflagellate ◽  
Gulf Of Gabes ◽  
Naive Bayesian ◽  
Gulf Of Gabès ◽  
Naïve Bayesian

A Bayesian network approach to determine environmental factors controlling Karenia selliformis occurrences and blooms in the Gulf of Gabès, Tunisia

Harmful Algae ◽  
2017 ◽  
Vol 63 ◽  
pp. 119-132 ◽  
Author(s):  
Wafa Feki-Sahnoun ◽  
Asma Hamza ◽  
Hasna Njah ◽  
Nouha Barraj ◽  
Mabrouka Mahfoudi ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Bayesian Network ◽  
Network Approach ◽  
Gulf Of Gabes ◽  
Gulf Of Gabès

scholarly journals Detecting Bank False Account Based on Naive Bayesian Network

2020 ◽  
Vol 768 ◽  
pp. 072074
Author(s):  
Weiqing Wan ◽  
Qingyan Zeng ◽  
Zhicheng Wen
Keyword(s):  
Bayesian Network ◽  
Naive Bayesian ◽  
Naïve Bayesian

Domains of competence of the semi-naive Bayesian network classifiers

2014 ◽  
Vol 260 ◽  
pp. 120-148 ◽  
Author(s):  
M. Julia Flores ◽  
José A. Gámez ◽  
Ana M. Martínez
Keyword(s):  
Bayesian Network ◽  
Naive Bayesian ◽  
Bayesian Network Classifiers ◽  
Naïve Bayesian

A Novel Discriminative Naive Bayesian Network for Classification

2007 ◽  
pp. 1-12 ◽  
Author(s):  
Kaizhu Huang ◽  
Zenglin Xu ◽  
Irwin King ◽  
Michael R. Lyu ◽  
Zhangbing Zhou
Keyword(s):  
Bayesian Network ◽  
Discriminative Training ◽  
Support Vector ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Training Approach ◽  
Vector Machines ◽  
Series Of Experiments ◽  
Classification Tasks ◽  
Real World Datasets

Naive Bayesian network (NB) is a simple yet powerful Bayesian network. Even with a strong independency assumption among the features, it demonstrates competitive performance against other state-of-the-art classifiers, such as support vector machines (SVM). In this chapter, we propose a novel discriminative training approach originated from SVM for deriving the parameters of NB. This new model, called discriminative naive Bayesian network (DNB), combines both merits of discriminative methods (e.g., SVM) and Bayesian networks. We provide theoretic justifications, outline the algorithm, and perform a series of experiments on benchmark real-world datasets to demonstrate our model’s advantages. Its performance outperforms NB in classification tasks and outperforms SVM in handling missing information tasks.

Optimization of Dynamic Naive Bayesian Network Classifier with Continuous Attributes

2012 ◽  
Vol 11 (1) ◽  
pp. 676-679
Author(s):  
Rei-Jie Du ◽  
Shuang-Cheng Wang ◽  
Han-Xing Wang ◽  
Cui-Ping Leng
Keyword(s):  
Bayesian Network ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Bayesian Network Classifier
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