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Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks

Fayroz F. Sherif, Nourhan Zayed, Mahmoud Fakhr
2015 Advances in Bioinformatics  
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.  ...  Acknowledgments Data collection and sharing of this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (  ... 
doi:10.1155/2015/639367 pmid:26366461 pmcid:PMC4561111 fatcat:jsrmx6obkjal7pabpabidwyvve

A New Bayesian Network Structure for Classification Tasks [chapter]

Michael G. Madden
2002 Lecture Notes in Computer Science  
Initial experiments have compared the performance of the PBN algorithm with Naïve Bayes, Tree-Augmented Naïve Bayes and a general Bayesian network algorithm (K2).  ...  The PBN is designed to be used for classification tasks, and accordingly the algorithm constructs an approximate Markov blanket around a classification node.  ...  Cheng and Greiner [3] evaluate the performance of two other network structures.  ... 
doi:10.1007/3-540-45750-x_27 fatcat:ixkr43cvfnfwzmed2t246l2w74

Application of Data Mining Using Bayesian Belief Network To Classify Quality of Web Services

M.Swami Das, Ramakanta Mohanty, D. Vijayalakshmi, Govardhan A
2013 International Journal of Computer Science and Informatics  
In this paper, we employed Naïve Bayes, Augmented Naïve Bayes, Tree Augmented Naïve Bayes, Sons & Spouses, Markov Blanket, Augmented Markov Blanket, Semi Supervised and Bayesian network techniques to rank  ...  The Bayesian Network is demonstrated on a dataset taken from literature. The dataset consists of 364 web services whose quality is described by 9 attributes.  ...  MARKOV BLANKET This algorithm searches for the nodes that belong to the Markov Blanket of the target: fathers, sons and spouses nodes.  ... 
doi:10.47893/ijcsi.2013.1109 fatcat:3ayagvku25h7bnyemljshmxeti

Hybrid Feature Selection for Modeling Intrusion Detection Systems [chapter]

Srilatha Chebrolu, Ajith Abraham, Johnson P. Thomas
2004 Lecture Notes in Computer Science  
We investigated the performance of two feature selection algorithms involving Bayesian Networks (BN) and Classification and Regression Trees (CART) and an ensemble of BN and CART.  ...  Some of the features may be redundant or contribute little (if anything) to the detection process.  ...  When using a BN classifier on complete data, the Markov blanket of the class node forms feature selection and all features outside the Markov blanket are deleted from the BN.  ... 
doi:10.1007/978-3-540-30499-9_158 fatcat:a4rbb4fvnvae5d6crjdtomah7e

Diagnosis of Cardiovascular Diseases with Bayesian Classifiers

Mahmoud Fakhr, Alaa Elsayad
2015 Journal of Computer Science  
In this study, we evaluate the performance of Bayesian classifier (BN) in predicting the risk of cardiovascular disease.  ...  This study evaluates two Bayesian network classifiers; Tree Augmented Naïve Bayes and the Markov Blanket Estimation and their prediction accuracies are benchmarked against the Support Vector Machine.  ...  Mahmoud Fakhr: Guided the analysis and the interpretation of the results, validated the experimental results and made discussion, collecting the literature.  ... 
doi:10.3844/jcssp.2015.274.282 fatcat:dspvxdnggvgjnolzjlqqxthb7m

Classification of High Dimensionality Data through Feature Selection Using Markov Blanket

Junghye Lee, Chi-Hyuck Jun
2015 Industrial Engineering & Management Systems  
In this paper, we study the possibility of utilizing the Markov blanket discovery algorithm as a new feature selection method.  ...  The Markov blanket of a target variable is the minimal variable set for explaining the target variable on the basis of conditional independence of all the variables to be connected in a Bayesian network  ...  ACKNOWLEDGEMENTS This research was supported by a grant of the Korea Health technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and  ... 
doi:10.7232/iems.2015.14.2.210 fatcat:iqhfkcunejgatfgherretzfrla

On using Bayesian networks for complexity reduction in decision trees

Adriana Brogini, Debora Slanzi
2009 Statistical Methods & Applications  
In this paper we use the Bayesian network as a tool of explorative analysis: its theory guarantees that, given the structure and some assumptions, the Markov blanket of a variable is the minimal conditioning  ...  We use the Markov blanket of a target variable to extract the relevant features for constructing a decision tree (DT).  ...  We choose HITON algorithm as it is developed to improve the performance of others Markov blanket discovery algorithms in literature.  ... 
doi:10.1007/s10260-009-0116-1 fatcat:fdoh5muvd5ayho3hjkhejikkeu

Predicting the Severity of Breast Masses with Ensemble of Bayesian Classifiers

2010 Journal of Computer Science  
Problem statement: This study evaluated two different Bayesian classifiers; tree augmented Naive Bayes and Markov blanket estimation networks in order to build an ensemble model for prediction the severity  ...  Approach: Apply ensemble of Bayesian classifiers to predict the severity of breast masses.  ...  Two different implementations of Bayesian network have been applied on the mammographic mass dataset; tree augmented Naive Bayes and Markov blanket estimation learning algorithms.  ... 
doi:10.3844/jcssp.2010.576.584 fatcat:eoomx6iysve4tiq2hoeptnzlrm

Learning Bayesian Belief Network Classifiers: Algorithms and System [chapter]

Jie Cheng, Russell Greiner
2001 Lecture Notes in Computer Science  
Using a set of standard classification problems, we empirically evaluate the performance of various BN-based classifiers.  ...  This paper investigates the methods for learning predictive classifiers based on Bayesian belief networks (BN) -primarily unrestricted Bayesian networks and Bayesian multi-nets.  ...  Find the Markov blanket B ⊆ F of the class node. 3.  ... 
doi:10.1007/3-540-45153-6_14 fatcat:rvjewedkhbbqtinjtpaoqthga4

A Comparison of Feature-Selection Methods for Intrusion Detection [chapter]

Hai Thanh Nguyen, Slobodan Petrović, Katrin Franke
2010 Lecture Notes in Computer Science  
Bayesian network can be used to compute the conditional probability of one node, given the values assigned to the other nodes • From the constructed Bayesian network the Markov blanket of the feature T  ...  statistical characteristics of a data set directly • No learning algorithm involved -The wrapper model • Assesses the selected features by evaluating the performance of the classification algorithm  ... 
doi:10.1007/978-3-642-14706-7_19 fatcat:lsxqdubisrgnrbiyd6af7tliv4

Dependency networks based classifiers: learning models by using independence

José A. Gámez, Juan L. Mateo, José Miguel Puerta
2006 European Workshop on Probabilistic Graphical Models  
We show that this algorithm is as good as some state-of the-art Bayesian classifiers, like TAN and an implementation of the BAN model, and has, in addition, other interesting proprierties like scalability  ...  Because of these promising characteristics we analyse the usefulness of dependency networks-based Bayesian classifiers.  ...  Apart from this approach to learn Bayesian classifiers, it must be mentioned another one based on the Markov blanket concept.  ... 
dblp:conf/pgm/GamezMP06 fatcat:axy4thsg3bfofhz4i3nmvmznyu

Data Mining of Perishable Food Safety Sampling based on Voting

Anqi Hu, Tongjuan Liu
2018 DEStech Transactions on Computer Science and Engineering  
Different models had been made, which by selecting the neural network algorithm, classification and regression tree algorithm and Bayesian network algorithm in the data mining software.  ...  The prediction model avoids the deterioration of perishable food flowing into the market, and ensures the safe transportation of perishable food.  ...  ACKNOWLEDGEMENT This work was supported by funding project for Youth Talent Cultivation Plan of Beijing City University under the grant number (CIT&TCD201504051), this work was supported by Beijing outstanding  ... 
doi:10.12783/dtcse/csae2017/17467 fatcat:43gppej6ojdevlx6txfhny5z4y

Parameter Estimation in Bayesian Networks Using Overlapping Swarm Intelligence

Nathan Fortier, John Sheppard, Shane Strasser
2015 Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO '15  
In the multi-swarm version, a swarm is assigned to the Markov blanket of each variable to be estimated, and competition is held between overlapping swarms.  ...  Results of comparing these new methods to several existing approaches indicate that the multi-swarm algorithm outperforms the competing approaches when compared using data generated from a variety of Bayesian  ...  Additionally, the performance of the OSI algorithms is worse when there was less overlap over the Markov blankets.  ... 
doi:10.1145/2739480.2754793 dblp:conf/gecco/FortierSS15 fatcat:eljoo33rqfadfavpviveojtf3q

Transcriptional network classifiers

Hsun-Hsien Chang, Marco F Ramoni
2009 BMC Bioinformatics  
The validation of our classifier using clinical data demonstrates the promising value of our proposed approach for disease diagnosis.  ...  Results: Our system biology approach is carried out by the Bayesian networks framework.  ...  Given the genes under the Markov blanket, the phenotype is independent of the genes not covered by the Markov blanket.  ... 
doi:10.1186/1471-2105-10-s9-s1 pmid:19761563 pmcid:PMC2745680 fatcat:q6le75pcsrfqddezjkakzaiwzi

Relevant based structure learning for feature selection

Hadi Zare, Mojtaba Niazi
2016 Engineering applications of artificial intelligence  
Furthermore the proposed approach is evaluated on a bunch of benchmark datasets based on the well-known classification algorithms.  ...  In line with the selection of the optimal subset of features through the proposed method, it provides us the Bayesian network classifier without the additional cost of model training on the selected subset  ...  We have exploited Bayesian networks as the optimal graph structures amidst features because of the straightforward identification of Markov blanket subsets through them.  ... 
doi:10.1016/j.engappai.2016.06.001 fatcat:s4solzsrkbefjdeycuup5m4yym
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