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Inference and Learning in Multi-dimensional Bayesian Network Classifiers
[chapter]
2007
Lecture Notes in Computer Science
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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
2016
Intelligent Data Analysis
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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
2013
Artificial Intelligence in Medicine
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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
2014
Biomedical Engineering Applications Basis and Communications
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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)
2012
Journal of Biomedical Informatics
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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
2014
Pattern Recognition Letters
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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
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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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
2014
Frontiers in Computational Neuroscience
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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
2016
Procedia Computer Science
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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
2014
2014 IEEE Conference on Computer Vision and Pattern Recognition
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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
2017
International Journal of Computational Intelligence Studies
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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
2009
2009 International Conference of Soft Computing and Pattern Recognition
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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
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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
2017
Biomarker Research
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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
2009
2009 Third International Conference on Sensor Technologies and Applications
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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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