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Inference and Learning in Multi-dimensional Bayesian Network Classifiers [chapter]

Peter R. de Waal, Linda C. van der Gaag
2007 Lecture Notes in Computer Science  
We describe the family of multi-dimensional Bayesian network classifiers which include one or more class variables and multiple feature variables.  ...  We further describe the learning problem for the subfamily of fully polytree-augmented multi-dimensional classifiers and show that its computational complexity is polynomial in the number of feature variables  ...  A multi-dimensional Bayesian network classifier includes one or more class variables and multiple feature variables.  ... 
doi:10.1007/978-3-540-75256-1_45 fatcat:f6jp5ozco5h6dk3pvrmyui4jmy

Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers

Hanen Borchani, Pedro Larrañaga, João Gama, Concha Bielza
2016 Intelligent Data Analysis  
To this end, we make use of multi-dimensional Bayesian network classifiers (MBCs) as models.  ...  The problem of mining multi-dimensional data streams, which includes multiple output class variables, is largely unexplored and only few streaming multi-dimensional approaches have been recently introduced  ...  Section 2 briefly defines the multi-dimensional classification problem, then introduces multi-dimensional Bayesian network classifiers.  ... 
doi:10.3233/ida-160804 fatcat:gj2joqyshfa5jbffq57wll7ktu

Predicting human immunodeficiency virus inhibitors using multi-dimensional Bayesian network classifiers

Hanen Borchani, Concha Bielza, Carlos Toro, Pedro Larrañaga
2013 Artificial Intelligence in Medicine  
Materials and methods: Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models especially designed to solve multi-dimensional classification problems, where each input  ...  Objective: Our aim is to use multi-dimensional Bayesian network classifiers in order to predict the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors given an input  ...  Definition 3 (Multi-dimensional Bayesian network classifier [1] ). An MBC is a Bayesian network B = (G, ) where the structure G = (V, A) has a restricted topology.  ... 
doi:10.1016/j.artmed.2012.12.005 pmid:23375464 fatcat:vnrvoblcyzeexgiwzrrnnxfgz4

PREDICTING THE EQ-5D FROM THE PARKINSON'S DISEASE QUESTIONNAIRE PDQ-8 USING MULTI-DIMENSIONAL BAYESIAN NETWORK CLASSIFIERS

Hanen Borchani, Concha Bielza, Pablo Martinez-Martin, Pedro Larrañaga
2014 Biomedical Engineering Applications Basis and Communications  
In this paper, we propose a new probabilistic method to predict EQ-5D from PDQ-8 using multi-dimensional Bayesian network classi¯ers.  ...  Our approach is evaluated using ¯ve-fold cross-validation experiments carried out on a Parkinson's data set containing 488 patients, and is compared with two additional Bayesian network-based approaches  ...  Multi-dimensional Bayesian network classi¯ers In our study, the prediction of EQ-5D values from PDQ-8 is modeled as a multi-dimensional classi¯cation problem where each instance given by an input vector  ... 
doi:10.4015/s101623721450015x fatcat:zg2kf4qokzcqxoidvx7e2fmtcm

Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: An application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39)

Hanen Borchani, Concha Bielza, Pablo Martı´nez-Martı´n, Pedro Larrañaga
2012 Journal of Biomedical Informatics  
Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has  ...  The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression  ...  Section 2 briefly presents Bayesian networks and multi-dimensional Bayesian network classifiers; then introduces the proposed MB-MBC learning approach.  ... 
doi:10.1016/j.jbi.2012.07.010 pmid:22897950 fatcat:rakj73uysjd7lnkumxvtwpi4im

Multi-label classification with Bayesian network-based chain classifiers

L. Enrique Sucar, Concha Bielza, Eduardo F. Morales, Pablo Hernandez-Leal, Julio H. Zaragoza, Pedro Larrañaga
2014 Pattern Recognition Letters  
In this paper we introduce a method for chaining Bayesian classifiers that combines the strengths of classifier chains and Bayesian networks for multi-label classification.  ...  A Bayesian network is induced from data to: (i) represent the probabilistic dependency relationships between classes, (ii) constrain the number of class variables used in the chain classifier by considering  ...  Multi-dimensional Bayesian network classifiers A multi-dimensional Bayesian network classifier (MBC) over a set V ¼ fZ 1 ; . . . ; Z n g; n P 1, of discrete random variables is a Bayesian network B ¼ ðG  ... 
doi:10.1016/j.patrec.2013.11.007 fatcat:oxm6ddw2kbhuhbrtyza75akzei

Deep Convolutional Neural Network Using Triplets of Faces, Deep Ensemble, and Score-Level Fusion for Face Recognition

Bong-Nam Kang, Yonghyun Kim, Daijin Kim
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
This paper proposes a new face verification method that uses multiple deep convolutional neural networks (DCNNs) and a deep ensemble, that extracts two types of low dimensional but discriminative and high-level  ...  In addition to further increase the accuracy, we combine the proposed method and high dimensional LBP based joint Bayesian method, and achieved 99.08% accuracy on the LFW.  ...  We used the joint Bayesian method as a classifier.  ... 
doi:10.1109/cvprw.2017.89 dblp:conf/cvpr/KangKK17 fatcat:4nuq242yrndjxgdmxbnqsbaowi

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

Bojan Mihaljević, Concha Bielza, Ruth Benavides-Piccione, Javier DeFelipe, Pedro Larrañaga
2014 Frontiers in Computational Neuroscience  
We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them.  ...  Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels.  ...  Multi-dimensional Bayesian network classifiers Borchani et al., 2013) can balance model complexity and the modeling of dependencies.  ... 
doi:10.3389/fncom.2014.00150 pmid:25505405 pmcid:PMC4243564 fatcat:3ri7keabbrafzcpcypvsqmam2u

Selecting Learning Algorithms for Simultaneous Identification of Depression and Comorbid Disorders

Blessing Ojeme, Audrey Mbogho
2016 Procedia Computer Science  
This paper reports the findings of a study based on data collected in Nigeria to investigate the simultaneous identification of depression and co-occuring physical illness using a multi-dimensional Bayesian  ...  network classification approach.  ...  Section 1.2 describes the identification of depression as a multi-dimensional problem and its proposed solution using multi-dimensional Bayesian network classifier.  ... 
doi:10.1016/j.procs.2016.08.174 fatcat:hp3yv6glvraflnrlvkwhzx4aza

Deep Learning Face Representation from Predicting 10,000 Classes

Yi Sun, Xiaogang Wang, Xiaoou Tang
2014 2014 IEEE Conference on Computer Vision and Pattern Recognition  
DeepID features are taken from the last hidden layer neuron activations of deep convolutional networks (ConvNets).  ...  We argue that DeepID can be effectively learned through challenging multi-class face identification tasks, whilst they can be generalized to other tasks (such as verification) and new identities unseen  ...  We also test the 4349-dimensional classifier outputs as features for face verification.  ... 
doi:10.1109/cvpr.2014.244 dblp:conf/cvpr/SunWT14 fatcat:gdyfboylxjhs5of4dag6ckwznq

SVM-based sentiment classification: a comparative study against state-of-the-art classifiers

Dionisios N. Sotiropoulos, Demitrios E. Pournarakis, George M. Giaglis
2017 International Journal of Computational Intelligence Studies  
In particular, we conducted an extensive experimental comparison where we tested the aforementioned classifier against a set of state-of-the-art machine learning classifiers on a benchmark dataset originating  ...  Our results present classification accuracy and execution time metrics for each classifier, revealing the superiority of the SVM learning paradigm in assigning patterns to the correct sentiment class.  ...  network (hidden layers = 5) 0.855 0.839 Multi-layer network (hidden layers = 3) 0.848 0.836 Multi-layer network (hidden layers = 1) 0.843 0.823 SVM linear 0.862 0.764 Bayesian network  ... 
doi:10.1504/ijcistudies.2017.086063 fatcat:e5kntbcq4jgfpee6bkd7myvkvm

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.  ...  classifiers, on a benchmark dataset containing 27 SCOP folds.  ...  research RBF Bayesian fuse 1 Multi Layer Perceptron neural network 2 General Regression Neural Networks 3 Radial Basis Function Network  ... 
doi:10.1109/socpar.2009.91 dblp:conf/socpar/HashemiSN09 fatcat:34awm2regzditdc7nik7jhmqry

Multi-Label Classification with PSO based Synthetic Minority Over-Sampling Technique (Psosmote) for Imbalanced Samples

2019 International journal of recent technology and engineering  
Trees (PCT), Hierarchy of Multi Label Classifier (HOMER) by taking the different metrics including precision, recall, F-measure, Accuracy and Error Rate.  ...  Then, Bayesian technique is combined with Random forest for multilabel classification (BARF-MLC) is to address the inherent label dependencies among samples such as ML-FOREST classifier, Predictive Clustering  ...  Multi-dimensional Bayesian network classifier is a Bayesian network (BN) of restrained topology aimed at resolving the multi-dimensional (along multi-label) classification problem.  ... 
doi:10.35940/ijrte.d8437.118419 fatcat:kunyeysggbdm3j4jv4n6vwqbni

Machine learning and systems genomics approaches for multi-omics data

Eugene Lin, Hsien-Yuan Lane
2017 Biomarker Research  
In this study, we reviewed the MLSG software frameworks and future directions with respect to multi-omics data analysis and integration.  ...  Software frameworks in MLSG are extensively employed to analyze hundreds of thousands of multi-omics data by high-throughput technologies.  ...  The MBI approach can then conduct the final multi-dimensional model from a particular machine learning algorithm (for example, Bayesian networks) with variables from the best models of each individual  ... 
doi:10.1186/s40364-017-0082-y pmid:28127429 pmcid:PMC5251341 fatcat:rfxwpblxwzacbggxwxrrndmmvi

Distributed Source Coding for Sensor Data Model and Estimation of Cluster Head Errors Using Bayesian and K-Near Neighborhood Classifiers in Deployment of Dense Wireless Sensor Networks

Vasanth Iyer, S. S. Iyengar, N. Balakrishnan, Vir. Phoha, G. Rama Murthy
2009 2009 Third International Conference on Sensor Technologies and Applications  
clustering were the node densities at the multi-hop MACS are unknown and not stored at the multi-hop nodes a priori.  ...  We study the effects of energy losses using cross-layer simulation of large sensor network MACS setup, the error rate which effect finding sufficient node densities to have reliable multi-hop communications  ...  Figure 1 illustrates the Bayesian classifier for pdf based clustering and multi-hop based passive clustering. II. DATA MODELS A.  ... 
doi:10.1109/sensorcomm.2009.11 fatcat:qnkjolajxjg5fpntyzcrrw3ibq
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