A Top-down Attention Model Using Naive Bayesian Network

2013 ◽  
Vol 7 (2) ◽  
pp. 205
Author(s):  
Kyung-Joo Cheoi
Keyword(s):  
Bayesian Network ◽  
Top Down ◽  
Attention Model ◽  
Naive Bayesian ◽  
Naïve Bayesian

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

scholarly journals Universal Target Learning: An Efficient and Effective Technique for Semi-Naive Bayesian Learning

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 729 ◽  
Author(s):  
SiQi Gao ◽  
Hua Lou ◽  
LiMin Wang ◽  
Yang Liu ◽  
Tiehu Fan
Keyword(s):  
Bayesian Network ◽  
Bayesian Learning ◽  
Training Data ◽  
Effective Technique ◽  
Bayesian Techniques ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Log Likelihood ◽  
Negative Effect ◽  
Bayesian Network Classifier

To mitigate the negative effect of classification bias caused by overfitting, semi-naive Bayesian techniques seek to mine the implicit dependency relationships in unlabeled testing instances. By redefining some criteria from information theory, Target Learning (TL) proposes to build for each unlabeled testing instance P the Bayesian Network Classifier BNC P , which is independent and complementary to BNC T learned from training data T . In this paper, we extend TL to Universal Target Learning (UTL) to identify redundant correlations between attribute values and maximize the bits encoded in the Bayesian network in terms of log likelihood. We take the k-dependence Bayesian classifier as an example to investigate the effect of UTL on BNC P and BNC T . Our extensive experimental results on 40 UCI datasets show that UTL can help BNC improve the generalization performance.

scholarly journals Students' learning style detection using tree augmented naive Bayes

2018 ◽  
Vol 5 (7) ◽  
pp. 172108 ◽  
Author(s):  
Ling Xiao Li ◽  
Siti Soraya Abdul Rahman
Keyword(s):  
Learning Styles ◽  
Bayesian Network ◽  
Learning Style ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Online Learning Environment ◽  
Detection Accuracy ◽  
Naive Bayesian ◽  
Bayes Network ◽  
Naïve Bayesian

Students are characterized according to their own distinct learning styles. Discovering students' learning style is significant in the educational system in order to provide adaptivity. Past researches have proposed various approaches to detect the students’ learning styles. Among all, the Bayesian network has emerged as a widely used method to automatically detect students' learning styles. On the other hand, tree augmented naive Bayesian network has the ability to improve the naive Bayesian network in terms of better classification accuracy. In this paper, we evaluate the performance of the tree augmented naive Bayesian in automatically detecting students’ learning style in the online learning environment. The experimental results are promising as the tree augmented naive Bayes network is shown to achieve higher detection accuracy when compared to the Bayesian network.

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