A New Bayesian Network Based on Gaussian Naive Bayes with Fuzzy Parameters for Training Assessment in Virtual Simulators

2020 ◽  
Author(s):  
Ronei M. Moraes ◽  
Jodavid A. Ferreira ◽  
Liliane S. Machado
Keyword(s):  
Bayesian Network ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Assessment ◽  
Fuzzy Parameters

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.

scholarly journals Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Fayroz F. Sherif ◽  
Nourhan Zayed ◽  
Mahmoud Fakhr
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Bayesian Network ◽  
Structure Learning ◽  
Naive Bayes ◽  
Strong Association ◽  
Naïve Bayes ◽  
Nucleotide Polymorphisms ◽  
Markov Blanket ◽  
Genome Wide

Single nucleotide polymorphisms (SNPs) contribute most of the genetic variation to the human genome. SNPs associate with many complex and common diseases like Alzheimer’s disease (AD). Discovering SNP biomarkers at different loci can improve early diagnosis and treatment of these diseases. Bayesian network provides a comprehensible and modular framework for representing interactions between genes or single SNPs. Here, different Bayesian network structure learning algorithms have been applied in whole genome sequencing (WGS) data for detecting the causal AD SNPs and gene-SNP interactions. We focused on polymorphisms in the top ten genes associated with AD and identified by genome-wide association (GWA) studies. New SNP biomarkers were observed to be significantly associated with Alzheimer’s disease. These SNPs are rs7530069, rs113464261, rs114506298, rs73504429, rs7929589, rs76306710, and rs668134. The obtained results demonstrated the effectiveness of using BN for identifying AD causal SNPs with acceptable accuracy. The results guarantee that the SNP set detected by Markov blanket based methods has a strong association with AD disease and achieves better performance than both naïve Bayes and tree augmented naïve Bayes. Minimal augmented Markov blanket reaches accuracy of 66.13% and sensitivity of 88.87% versus 61.58% and 59.43% in naïve Bayes, respectively.

Selectively Fine-Tuning Bayesian Network Learning Algorithm

2016 ◽  
Vol 30 (08) ◽  
pp. 1651005 ◽  
Author(s):  
Amel Alhussan ◽  
Khalil El Hindi
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Classification Accuracy ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Fine Tuning ◽  
Network Learning ◽  
Probability Term ◽  
Fine Tune

In this work, we propose a Selective Fine-Tuning algorithm for Bayesian Networks (SFTBN). The aim is to enhance the accuracy of Bayesian Network (BN) classifiers by finding better estimations for the probability terms used by the classifiers. The algorithm augments a BN learning algorithm with a fine-tuning stage that aims to more accurately estimate the probability terms used by the BN. If the value of a probability term causes a misclassification of a training instances and falls outside its valid range then we update (fine-tune) that value. The amount of such an update is proportional to the distance between the value and its valid range. We use the algorithm to fine-tune several forms of BNs: the Naive Bayes (NB), Tree Augmented Naive Bayes (TAN), and Bayesian Augmented Naive Bayes (BAN) models. Our empirical experiments indicate that the SFTBN algorithm improves the classification accuracy of BN classifiers. We also generalized the original fine-tuning algorithm of Naive Bayesian (FTNB) for BN models. We empirically compare the two algorithms, and the empirical results show that while FTNB is more accurate than SFTBN for fine-tuning NB classifiers, SFTBN is more accurate for fine-tuning BNs than the adapted version of FTNB.

scholarly journals Selected Artificial Intelligence Methods in the Risk Analysis of Damage to Masonry Buildings Subject to Long-Term Underground Mining Exploitation

Minerals ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 958
Author(s):  
Leszek Chomacki ◽  
Janusz Rusek ◽  
Leszek Słowik
Keyword(s):  
Artificial Intelligence ◽  
Bayesian Network ◽  
Network Structure ◽  
Naive Bayes ◽  
Underground Mining ◽  
Naïve Bayes ◽  
Engineering Practice ◽  
Masonry Buildings ◽  
Mining Areas ◽  
Bayesian Network Structure

This paper presents an advanced computational approach to assess the risk of damage to masonry buildings subjected to negative kinematic impacts of underground mining exploitation. The research goals were achieved using selected tools from the area of artificial intelligence (AI) methods. Ultimately, two models of damage risk assessment were built using the Naive Bayes classifier (NBC) and Bayesian Networks (BN). The first model was used to compare results obtained using the more computationally advanced Bayesian network methodology. In the case of the Bayesian network, the unknown Directed Acyclic Graph (DAG) structure was extracted using Chow-Liu’s Tree Augmented Naive Bayes (TAN-CL) algorithm. Thus, one of the methods involving Bayesian Network Structure Learning from data (BNSL) was implemented. The application of this approach represents a novel scientific contribution in the interdisciplinary field of mining and civil engineering. The models created were verified with respect to quality of fit to observed data and generalization properties. The connections in the Bayesian network structure obtained were also verified with respect to the observed relations occurring in engineering practice concerning the assessment of the damage intensity to masonry buildings in mining areas. This allowed evaluation of the model and justified the utility of the conducted research in the field of protection of mining areas. The possibility of universal application of the Bayesian network, both in the case of damage prediction and diagnosis of its potential causes, was also pointed out.

scholarly journals Discriminative Structure Learning of Bayesian Network Classifiers from Training Dataset and Testing Instance

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 489 ◽  
Author(s):  
Limin Wang ◽  
Yang Liu ◽  
Musa Mammadov ◽  
Minghui Sun ◽  
Sikai Qi
Keyword(s):  
Bayesian Network ◽  
Learning Strategy ◽  
Structure Learning ◽  
Naive Bayes ◽  
Search Space ◽  
Naïve Bayes ◽  
Bayesian Classifier ◽  
Training Data ◽  
Training Dataset ◽  
Bayesian Network Classifiers

Over recent decades, the rapid growth in data makes ever more urgent the quest for highly scalable Bayesian networks that have better classification performance and expressivity (that is, capacity to respectively describe dependence relationships between attributes in different situations). To reduce the search space of possible attribute orders, k-dependence Bayesian classifier (KDB) simply applies mutual information to sort attributes. This sorting strategy is very efficient but it neglects the conditional dependencies between attributes and is sub-optimal. In this paper, we propose a novel sorting strategy and extend KDB from a single restricted network to unrestricted ensemble networks, i.e., unrestricted Bayesian classifier (UKDB), in terms of Markov blanket analysis and target learning. Target learning is a framework that takes each unlabeled testing instance P as a target and builds a specific Bayesian model Bayesian network classifiers (BNC) P to complement BNC T learned from training data T . UKDB respectively introduced UKDB P and UKDB T to flexibly describe the change in dependence relationships for different testing instances and the robust dependence relationships implicated in training data. They both use UKDB as the base classifier by applying the same learning strategy while modeling different parts of the data space, thus they are complementary in nature. The extensive experimental results on the Wisconsin breast cancer database for case study and other 10 datasets by involving classifiers with different structure complexities, such as Naive Bayes (0-dependence), Tree augmented Naive Bayes (1-dependence) and KDB (arbitrary k-dependence), prove the effectiveness and robustness of the proposed approach.

scholarly journals PERBANDINGAN METODE NAÏVE BAYES DAN EQUIVALENCE CLASSES DALAM MENENTUKAN STRUKTUR BAYESIAN NETWORK

2020 ◽  
Vol 6 (2) ◽  
pp. 132
Author(s):  
Yussyafrida Choiriizzati Rochmana ◽  
Maftahatul Hakimah ◽  
Farida Farida
Keyword(s):  
Bayesian Network ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Equivalence Classes

Bayesian Network merupakan model yang termasuk dalam klasifikasi bayes, dimana metode ini mengasumsikan bahwa nilai variabel independen memiliki ketergantungan dengan nilai variabel lain. Bayesian Network memiliki keunggulan yaitu dapat memodelkan hubungan antar variabel dengan menggunakan graf atau semacam penggambaran alur hubungan antar variabel. Terdapat beberapa metode yang digunakan untuk menentukan struktur bayesian network. Metode pembentukan struktur jaringan Bayesian network pada penelitian ini adalah metode naïve bayes dan equivalence classes. Kedua metode pembentukan struktur ini diterapkan untuk klasifikasi kelayakan peminjaman dana Usaha Kecil Mikro Menengah (UMKM). Pada struktur metode naïve bayes variabel dependen menjadi pusat dari variabel independen sedangkan pada struktur metode equivalence classes setiap variabel memiliki hubungan antar variabel lain. Hasil pengujian dari metode naïve bayes dan equivalence classes dalam pembentukan struktur Bayesian network secara rata-rata adalah metode equivalence classes 79,53% dan naïve bayes 80,93%.

Sign in / Sign up

Export Citation Format

Share Document