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A New Ensemble Learning Algorithm Combined with Causal Analysis for Bayesian Network Structural Learning

Ming Li, Ren Zhang, Kefeng Liu
2020 Symmetry  
Aiming at the problems of local optimum and low generalization in existing SS algorithms, we introduce the Ensemble Learning (EL) and causal analysis to propose a new BN structural learning algorithm named  ...  The Bayesian Network (BN) has been widely applied to causal reasoning in artificial intelligence, and the Search-Score (SS) method has become a mainstream approach to mine causal relationships for establishing  ...  Basic Theory Bayesian Network The Bayesian Network (BN), also known as the Bayesian reliability network, is not only a graphical expression of causal relationships among variables but also a probabilistic  ... 
doi:10.3390/sym12122054 fatcat:nbbkf3ahrbd3voniqhrmaav5ym

PEnBayes: A Multi-Layered Ensemble Approach for Learning Bayesian Network Structure from Big Data

Yan Tang, Jianwu Wang, Mai Nguyen, Ilkay Altintas
2019 Sensors  
To address these shortcomings, we introduce a novel parallel learning approach called PEnBayes (Parallel Ensemble-based Bayesian network learning).  ...  Lastly, PEnBayes merges the local BN Structures into a global network structure using the structure ensemble method at the global layer.  ...  Number of Local Learners Conclusions In this paper, we propose a novel parallel learning approach called PEnBayes (Parallel Ensemble-based Bayesian network learning) for a learning Bayesian network (  ... 
doi:10.3390/s19204400 fatcat:bqwgmciahrboxd5gkhfgs33ltm

Learning ensembles of Continuous Bayesian Networks: An application to rainfall prediction

Scott Hellman, Amy McGovern, Ming Xue
2012 2012 Conference on Intelligent Data Understanding  
We introduce Ensembled Continuous Bayesian Networks (ECBN), an ensemble approach to learning salient dependence relationships and to predicting values for continuous data.  ...  We use linear Gaussian distributions within our ensembles, providing efficient network-level inference. By ensembling these networks, we are able to represent nonlinear relationships.  ...  INTRODUCTION We introduce Ensembled Continuous Bayesian Networks (ECBN), an ensemble approach for prediction of continuous variables using Bayesian networks.  ... 
doi:10.1109/cidu.2012.6382191 dblp:conf/cidu/HellmanMX12 fatcat:a3ca5tcs6fhh5h5p4zbcumhcke

Learning Markov Blankets for Continuous or Discrete Networks via Feature Selection [chapter]

Houtao Deng, Saylisse Davila, George Runger, Eugene Tuv
2011 Studies in Computational Intelligence  
Markov Blankets discovery algorithms are important for learning a Bayesian network structure. We present an argument that tree ensemble masking measures can provide an approximate Markov blanket.  ...  We compare our algorithm in the causal structure learning problem to other well-known feature selection methods, and to a Bayesian local structure learning algorithm.  ...  Introduction Structure learning in Bayesian networks is an important step for causal inference and Markov Blanket causal discovery algorithms can be helpful for learning the structure of Bayesian networks  ... 
doi:10.1007/978-3-642-22910-7_7 fatcat:joemcoidjzgwzi2vaujvva2qhq

Boosted Bayesian network classifiers

Yushi Jing, Vladimir Pavlović, James M. Rehg
2008 Machine Learning  
We also demonstrate that structure learning is beneficial in the construction of boosted Bayesian network classifiers.  ...  We show that boosted Bayesian network classifiers encompass the basic generative models in isolation, but improve their classification performance when the model structure is suboptimal.  ...  This section explores techniques in learning an ensemble Bayesian network classifier with heterogeneous structures.  ... 
doi:10.1007/s10994-008-5065-7 fatcat:mnophkkpnzfgdevfmzpiy26tzy

Efficient discriminative learning of Bayesian network classifier via boosted augmented naive Bayes

Yushi Jing, Vladimir Pavlović, James M. Rehg
2005 Proceedings of the 22nd international conference on Machine learning - ICML '05  
The use of Bayesian networks for classification problems has received significant recent attention.  ...  Recent approaches to optimizing the classification performance during parameter or structure learning show promise, but lack the favorable computational properties of maximum likelihood learning.  ...  Ensemble output: sign k β k f θ k (x a ) Boosted Parameter Learning Ensemble Model Instead of maximizing the CLL score for a single Bayesian network model, we are going to take the ensemble approach  ... 
doi:10.1145/1102351.1102398 dblp:conf/icml/JingPR05 fatcat:copufpdozje4xl65ilkaunzpve

Ensemble Bayesian Decision Making with Redundant Deep Perceptual Control Policies [article]

Keuntaek Lee, Ziyi Wang, Bogdan I. Vlahov, Harleen K. Brar, Evangelos A. Theodorou
2020 arXiv   pre-print
This work presents a novel ensemble of Bayesian Neural Networks (BNNs) for control of safety-critical systems.  ...  Our proposed ensemble of BNNs shows successful task performance even in the event of multiple sensor failures.  ...  In our work, we trained our networks with batch imitation learning. III. ENSEMBLE BAYESIAN DECISION MAKING A.  ... 
arXiv:1811.12555v3 fatcat:ebmuubbxjbaslebukep2xzldpi

Protein Fold Pattern Recognition Using Bayesian Ensemble of RBF Neural Networks

Homa Baradaran Hashemi, Azadeh Shakery, Mahdi Pakdaman Naeini
2009 2009 International Conference of Soft Computing and Pattern Recognition  
To deal with such a challenging problem, we use an ensemble classifier model by applying MLP and RBF Neural Networks and Bayesian ensemble method.  ...  Experimental results imply that RBF Neural Network holds better Correct Classification Rate (CCR) compared to other common classification methods such as MLP networks.  ...  Figure 4 . 4 Ensemble CCR of RBF network.  ... 
doi:10.1109/socpar.2009.91 dblp:conf/socpar/HashemiSN09 fatcat:34awm2regzditdc7nik7jhmqry

Sequential Bayesian Neural Subnetwork Ensembles [article]

Sanket Jantre, Sandeep Madireddy, Shrijita Bhattacharya, Tapabrata Maiti, Prasanna Balaprakash
2022 arXiv   pre-print
Deep neural network ensembles that appeal to model diversity have been used successfully to improve predictive performance and model robustness in several applications.  ...  We propose sequential ensembling of dynamic Bayesian neural subnetworks that systematically reduce model complexity through sparsity-inducing priors and generate diverse ensembles in a single forward pass  ...  We adopt the sparse BNN model of [30] to achieve the structural sparsity in Bayesian neural networks.  ... 
arXiv:2206.00794v1 fatcat:zke4v34plrgvnfsodhqimqbpti

The Case for Bayesian Deep Learning [article]

Andrew Gordon Wilson
2020 arXiv   pre-print
to Bayesian methods, but can be seen as approximate Bayesian marginalization. (3) The structure of neural networks gives rise to a structured prior in function space, which reflects the inductive biases  ...  advances for Bayesian deep learning provide improvements in accuracy and calibration compared to standard training, while retaining scalability.  ...  However, when we combine a vague prior over parameters p(w) with a structured function form f (x; w) such as a convolutional neural network (CNN), we induce a structured prior distribution over functions  ... 
arXiv:2001.10995v1 fatcat:zuybmxdcxnhb7amp44bthsat7m

Challenges and Opportunities in Approximate Bayesian Deep Learning for Intelligent IoT Systems [article]

Meet P. Vadera, Benjamin M. Marlin
2021 arXiv   pre-print
Approximate Bayesian deep learning methods hold significant promise for addressing several issues that occur when deploying deep learning components in intelligent systems, including mitigating the occurrence  ...  In this paper, we present a range of approximate Bayesian inference methods for supervised deep learning and highlight the challenges and opportunities when applying these methods on current edge hardware  ...  Structured pruning can also be composed with approximate Bayesian deep learning methods in multiple ways.  ... 
arXiv:2112.01675v1 fatcat:okknsw5gifhl7ghzt4cnuchuje

Using Bayesian networks for selecting classifiers in GP ensembles

C. De Stefano, G. Folino, F. Fontanella, A. Scotto di Freca
2014 Information Sciences  
This paper presents a novel approach for combining GP-based ensembles by means of a Bayesian Network.  ...  ; in the second, the responses of the ensemble are combined using a Bayesian network, which also implements a selection strategy to reduce the number of classifiers.  ...  Following this approach, a Bayesian network (BN) [35] was learned in order to estimate the conditional probability of each class, given the set of labels provided by the classifiers for each sample of  ... 
doi:10.1016/j.ins.2013.09.049 fatcat:4dor4ql7v5fohhtcisdoz54fqy

Using bayesian networks for selecting classifiers in GP ensembles

Claudio De Stefano, Gianluigi Folino, Francesco Fontanella, Alessandra Scotto di Freca
2011 Proceedings of the 13th annual conference companion on Genetic and evolutionary computation - GECCO '11  
This paper presents a novel approach for combining GP-based ensembles by means of a Bayesian Network.  ...  ; in the second, the responses of the ensemble are combined using a Bayesian network, which also implements a selection strategy to reduce the number of classifiers.  ...  Following this approach, a Bayesian network (BN) [35] was learned in order to estimate the conditional probability of each class, given the set of labels provided by the classifiers for each sample of  ... 
doi:10.1145/2001858.2001955 dblp:conf/gecco/StefanoFFF11 fatcat:wrbguafllbd2bfftlwfzn6u52y

Inner Ensembles: Using Ensemble Methods Inside the Learning Algorithm [chapter]

Houman Abbasian, Chris Drummond, Nathalie Japkowicz, Stan Matwin
2013 Lecture Notes in Computer Science  
Ensemble Methods represent an important research area within machine learning.  ...  Specifically, we show that the idea can be applied to different classes of learner such as Bayesian networks and K-means clustering.  ...  Learning Bayesian Network Structure Bayesian networks have been used for many applications: medicine, expert systems [17] and path finder systems [18] .  ... 
doi:10.1007/978-3-642-40994-3_3 fatcat:kk7ycgcyu5ehxfmyplkjsvtohm

Predicting the Severity of Breast Masses with Ensemble of Bayesian Classifiers

Elsayad
2010 Journal of Computer Science  
Results: The prediction accuracies of Bayesian ensemble was benchmarked against the well-known multilayer perceptron neural network and the ensemble had achieved a remarkable performance with 91.83% accuracy  ...  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  ...  Figure 3a shows the cumulative gain curves of the Bayesian models, the proposed ensemble and the neural network for test samples.  ... 
doi:10.3844/jcssp.2010.576.584 fatcat:eoomx6iysve4tiq2hoeptnzlrm
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