scholarly journals A causal Bayes net analysis of Glennan's mechanistic account of higher-level causation (and some consequences)

2020 ◽  
Author(s):  
Alexander Gebharter
Keyword(s):  
Bayes Net

scholarly journals Mineração de dados aplicada à classificação automática de gêneros musicais

2019 ◽  
Vol 11 (3) ◽  
pp. 47-58
Author(s):  
Paulo Sergio da Conceição Moreira ◽  
Denise Fukumi Tsunoda
Keyword(s):  
Machine Learning ◽  
Heavy Metal ◽  
Random Forest ◽  
Bayes Net

Tem por objetivo classificar gêneros musicais automaticamente por meio de algoritmos de Mineração de Dados, considerando descritores extraídos do sinal de áudio. Identifica na Last.fm as 150 músicas mais populares de sete gêneros musicais (Rock, Jazz, POP, Música Clássica, MPB, Heavy Metal e Samba). Mediante a extração de descritores relacionados ao sinal de áudio destas músicas, aplica os algoritmos Random Forest; Bayes Net; C4.5; KNN e as estratégias Bagging e Boosting para a classificação. Obtém como melhor resultado 66,67% de acerto com o algoritmo C4.5 para classificação entre Samba e MPB. Constata que a classificação de gêneros musicais se apresenta como um "problema interessante" para estudos que envolvem técnicas de Machine Learning. Estimula a continuidade de estudos semelhantes aplicando algoritmos baseados em Redes Neurais e Algoritmos Genéticos.

Soil Quality and Health – can it be quantified?

2020 ◽  
Author(s):  
Kirsty Hassall ◽  
Joanna Zawadzka ◽  
Alice Milne ◽  
Gordon Dailey ◽  
Jim Harris ◽  
...  
Keyword(s):  
Soil Quality ◽  
Conditional Probability ◽  
Expert Knowledge ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Bayes Net ◽  
Inference Network ◽  
Web App ◽  
National Databases ◽  
Net Framework

<p>Soil quality and health (SQH) are terms used extensively to characterise soils. However, the exact definitions of quality and health are often qualitative with differing meanings to different stakeholders. Collecting and combining these differing viewpoints is a non-trivial task. In this work, we will discuss how we have used the Bayes Net framework to define a hierarchical structure that enables a subjective concept such as soil quality and health to be quantified from multiple sources of information including diverse sources of expert knowledge and linking this through to national databases.</p><p>Information within a Bayes Net is encapsulated through a set of conditional probability tables that describe the conditional dependencies of all variables of interest. It is well known that humans are particularly poor at estimating such probabilities which, when a Bayes Net relies upon experts from differing disciplines and stakeholders from disparate application areas to quantify their beliefs through these conditional probability tables, is often a major limitation to these techniques. Here, we demonstrate an elicitation web app that mitigates some of the difficulties associated with quantifying subjective opinion. Moreover, we show how an inference network of known associations aids in the extraction of information from increasingly subjective sources within the hierarchical framework.</p>

scholarly journals Mind change optimal learning of Bayes net structure from dependency and independency data

2010 ◽  
Vol 208 (1) ◽  
pp. 63-82 ◽  
Author(s):  
Oliver Schulte ◽  
Wei Luo ◽  
Russell Greiner
Keyword(s):  
Optimal Learning ◽  
Bayes Net

A graphical (Bayes net) molecular model of the entire DNA STR process to aid interpretation

2006 ◽  
Vol 1288 ◽  
pp. 468-470 ◽  
Author(s):  
Peter Gill ◽  
James Curran ◽  
Keith Elliot
Keyword(s):  
Molecular Model ◽  
Bayes Net

scholarly journals Analysis of WEKA data mining algorithms Bayes net, random forest, MLP and SMO for heart disease prediction system: A case study in Iraq

2021 ◽  
Vol 11 (6) ◽  
pp. 5229
Author(s):  
Rana Riad K. AL-Taie ◽  
Basma Jumaa Saleh ◽  
Ahmed Yousif Falih Saedi ◽  
Lamees Abdalhasan Salman
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Random Forest ◽  
Multilayer Perceptron ◽  
Data Extraction ◽  
Sequential Minimal Optimization ◽  
Health Issues ◽  
Sequence Modeling ◽  
Data Mining Algorithms ◽  
Bayes Net

Data mining is defined as a search through large amounts of data for valuable information. The association rules, grouping, clustering, prediction, sequence modeling is some essential and most general strategies for data extraction. The processing of data plays a major role in the healthcare industry's disease detection. A variety of disease evaluations should be required to diagnose the patient. However, using data mining strategies, the number of examinations should be decreased. This decreased examination plays a crucial role in terms of time and results. Heart disease is a death-provoking disorder. In this recent instance, health issues are immense because of the availability of health issues and the grouping of various situations. Today, secret information is important in the healthcare industry to make decisions. For the prediction of cardiovascular problems, (Weka 3.8.3) tools for this analysis are used for the prediction of data extraction algorithms like sequential minimal optimization (SMO), multilayer perceptron (MLP), random forest and Bayes net. The data collected combine the prediction accuracy results, the receiver operating characteristic (ROC) curve, and the PRC value. The performance of Bayes net (94.5%) and random forest (94%) technologies indicates optimum performance rather than the sequential minimal optimization (SMO) and multilayer perceptron (MLP) methods.

scholarly journals A New Approach of Rough Set Theory for ‎Feature Selection and Bayes Net Classifier ‎Applied on Heart Disease Dataset

2017 ◽  
Vol 26 (2) ◽  
pp. 15-26
Author(s):  
Eman S. Al-Shamery ◽  
Ali A.Rahoomi Al-Obaidi
Keyword(s):  
Feature Selection ◽  
Heart Disease ◽  
Rough Set ◽  
Rough Set Theory ◽  
Heart Diseases ◽  
Data Sets ◽  
Features Selection ◽  
New Approach ◽  
Bayes Net ◽  
Fold Cross Validation

In this paper a new approach of rough set features selection has been proposed. Feature selection has been used for several reasons a) decrease time of prediction b) feature possibly is not found c) present of feature case bad prediction. Rough set has been used to select most significant features. The proposed rough set has been applied on heart diseases data sets. The main problem is how to predict patient has heart disease or not depend on given features. The problem is challenge, because it cannot determine decision directly .Rough set has been modified to get attributes for prediction by ignored unnecessary and bad features. Bayes net has been used for classified method. 10-fold cross validation is used for evaluation. The Correct Classified Instances were 82.17, 83.49, and 74.58 when use full, 12, 7 length of attributes respectively. Traditional rough set has been applied, the minimum Correct Classified Instances were 58.41 and 81.51 when use 2 length of attributes respectively

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