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Bayesian networks classifiers for gene-expression data
2011
2011 11th International Conference on Intelligent Systems Design and Applications
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Second, we propose a preprocessing scheme for gene expression data, to induce Bayesian classifiers. ...
In this work, we study the application of Bayesian networks classifiers for gene expression data in three ways: first, we made an exhaustive state-of-art of Bayesian classifiers and Bayesian classifiers ...
In Bayesian multinets we have a distinguished variable and a Bayesian network for each value of this variable. ...
doi:10.1109/isda.2011.6121822
dblp:conf/isda/CamposCCM11
fatcat:ijh5jeu4tvfe3jwg3oczcincnm
Bayesian Conditional Gaussian Network Classifiers with Applications to Mass Spectra Classification
[article]
2013
arXiv
pre-print
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In this work we introduce Bayesian Conditional Gaussian Network Classifiers, which efficiently perform exact Bayesian averaging over the parameters. ...
Classifiers based on probabilistic graphical models are very effective. In continuous domains, maximum likelihood is usually used to assess the predictions of those classifiers. ...
Eventually, continuous variables can be discretized so as to use discrete Bayesian network classifiers. Alternatively, continuous variables can be directly modeled. ...
arXiv:1308.6181v1
fatcat:wqefzgtdfjfxtffbczst26633y
Advanced Approach for Distributions Parameters Learning in Bayesian Networks with Gaussian Mixture Models and Discriminative Models
2023
Mathematics
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inference for networks that require edges from continuous nodes to discrete ones. ...
Bayesian networks are a powerful tool for modelling multivariate random variables. ...
Classification Models in Parameters Learning In general, Bayesian network structure search methods allow continuous variables to be parents of discrete variables. ...
doi:10.3390/math11020343
fatcat:6qrfkyhkrrhgpe3xb4iq4kaaku
Bayesian Model Averaging of Bayesian Network Classifiers for Intrusion Detection
2014
2014 IEEE 38th International Computer Software and Applications Conference Workshops
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To alleviate these problems, we build a Bayesian classifier by Bayesian Model Averaging(BMA) over the k-best BN classifiers, called Bayesian Network Model Averaging (BNMA) classifier. ...
Bayesian network (BN) classifiers with powerful reasoning capabilities have been increasingly utilized to detect intrusion with reasonable accuracy and efficiency. ...
Thus, continuous features need to be discretized before being used to build a classifier. ...
doi:10.1109/compsacw.2014.25
dblp:conf/compsac/XiaoCC14
fatcat:myeqxkguabhvlm7d2v72xkt34e
Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers
2017
Discrete Dynamics in Nature and Society
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The structure of TAN classifier for incident detection is learned from data. The discretization of continuous attributes is processed using an entropy-based method automatically. ...
This study develops a tree augmented naive Bayesian (TAN) classifier based incident detection algorithm. ...
for the discretization of continuous variables completely depending on the data. ...
doi:10.1155/2017/8523495
fatcat:j7cb2yxb7nf6jjdjnosivxzeke
A Bayesian network for combining descriptors: application to symbol recognition
2009
International Journal on Document Analysis and Recognition
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This model also enables to handle both discrete and continuous-valued variables. ...
In fact, in order to improve the recognition rate, we have combined two kinds of features: discrete features (corresponding to shape measures) and continuous features (corresponding to shape descriptors ...
Bayesian networks
Definitions Formally, a Bayesian network for a set of random variables V (continuous or/and discrete) is a pair B = G, . ...
doi:10.1007/s10032-009-0103-y
fatcat:qaf3qc6iuvdnpak7gyzapff6qa
Multidimensional continuous time Bayesian network classifiers
2021
International Journal of Intelligent Systems
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This model extends continuous time Bayesian networks to the multidimensional classification problem, which are able to explicitly represent the behavior of time series that This is an open access article ...
In this paper, a novel probabilistic graphical model is proposed, which is able to classify a discrete multivariate temporal sequence into multiple class variables while modeling their dependencies. ...
ENDNOTES * Sequences may have different timestamps, superscript l is omitted from t for simplicity. † The state domain of each variable can be different, but the subscript i is omitted from k for simplicity ...
doi:10.1002/int.22611
fatcat:y6o2khcg35hw3pea2kzgyhqwai
Classification of Village Development Index at Regency/Municipality Level Using Bayesian Network Approach with K-Means Discretization
2022
Jurnal Aplikasi Statistika & Komputasi Statistik
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Further, we combine the discretization using the K-Means clustering method to handle the continuous nature of retrieved data. ...
In this paper, we build and analyze the Bayesian network methods to classify the village development index at regency/municipality and gain a better understanding of the causal relationships between independent ...
The Bayesian network models built from the k-means discrete data have better performance accuracy than the models built from the data without discretization. ...
doi:10.34123/jurnalasks.v14i1.390
fatcat:v4plfxpk4bg4nogvkht5m7yrua
Continuous time Bayesian network classifiers
2012
Journal of Biomedical Informatics
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A learning algorithm for the continuous time naive Bayes classifier and an exact inference algorithm for the class of continuous time Bayesian network classifiers are described. ...
Learning and inference for the class of continuous time Bayesian network classifiers are addressed, in the case where complete data are available. ...
Acknowledgments The authors would like to acknowledge the many helpful suggestions of the anonymous reviewers which helped to improve the paper clarity and quality with specific reference to numerical ...
doi:10.1016/j.jbi.2012.07.002
pmid:22846170
fatcat:4rerz43a3bhxbencjw4ewq3yjy
BAYES-NEAREST: A New Hybrid Classifier Combining Bayesian Network and Distance Based Algorithms
[chapter]
2003
Lecture Notes in Computer Science
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For those data bases in which some variables are continuous valued, automatic discretizations of the data are performed. ...
For those data bases in which some variables are continuous valued, automatic discretizations of the data are performed. ...
Discretization of continuous attributes Many classification algorithms require the classifying variables to be discretized, either because they need nominal values, or in order to reduce the computational ...
doi:10.1007/978-3-540-24580-3_24
fatcat:oy34exrntfcd3gd34vrer4733m
Comparing the Ability of Bayesian Networks and Adaboost for Predicting Financial Distress of Firms Listed on Tehran Stock Exchange (TSE)
2012
Social Science Research Network
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The aim of this study is to compare the ability of Bayesian networks and adaboost for predicting financial distress of firms listed on Tehran Stock Exchange (TSE). ...
But, Bayesian networks are more capable to predict financial distress of companies listed on TSE compare to Adaboost. ...
We used Uniform Widths method to convert continuous variables into discrete. During the discretization process, one problem that researchers face is to decide the number of states for discretization. ...
doi:10.2139/ssrn.2083617
fatcat:umh7ikrfcjhhdecrqwlkvexpwa
Discretization methods for Bayesian networks in the case of the earthquake
2021
Bulletin of Electrical Engineering and Informatics
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If normal conditions unsatisfied, we offer a solution, is to discretize continuous variables. Next, we can continue the process with the discrete Bayesian networks. ...
For data processing using hybrid and continuous Bayesian networks, all continuous variables must be normally distributed. ...
Therefore, we carry out a method of discretization for all continuous variables and continue the process using discrete Bayesian networks. ...
doi:10.11591/eei.v10i1.2007
fatcat:ywowr6ff2rgetjxx5meaeoz764
Learning continuous time Bayesian network classifiers
2014
International Journal of Approximate Reasoning
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Continuous time Bayesian network classifiers are designed for analyzing multivariate streaming data when time duration of events matters. ...
Numerical experiments show that the proposed approach outperforms dynamic Bayesian network classifiers and continuous time Bayesian network classifiers learned with log-likelihood. ...
The authors would like to thank Project Automation S.p.A. for funding the Ph.D. programme of Daniele Codecasa. ...
doi:10.1016/j.ijar.2014.05.005
fatcat:iw65vxnppvbjtck7vanmqivoc4
Comparison of Several Classifiers for the Detection of Polluting Smokes
2006
2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06)
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In this paper three types of classifiers are studied: two bayesian networks, a k-nearest neighbour classifier, and finally a linear model. ...
We assume in this paper that the signals are useful to classify the clouds and that we do not need other data. ...
Acknoledgement Results presented here are based on a joint work with the company Aloatec and we want to thank their head, Philippe Bourrier.
References [1] J. P. Benzécri. ...
doi:10.1109/cimca.2006.73
dblp:conf/cimca/GacquerDDP06
fatcat:m6alckbi5zfnxcdcgpotoddgdy
Incident Duration Prediction Based on Latent Gaussian Naive Bayesian classifier
2011
International Journal of Computational Intelligence Systems
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Therefore a continuous model based on latent Gaussian naive Bayesian (LGNB) classifier is developed in this paper, assuming duration fits a lognormal distribution. ...
According to the evidence sensitivity analysis of LGNB, the four classes of incidents classified by LGNB can be interpreted by the level of severity and complexity. ...
The authors would like to express their appreciation towards Dr. ...
doi:10.1080/18756891.2011.9727792
fatcat:dkfztugjjjfohjkp3k5esyqcqm
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