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EM Algorithm for Symmetric Causal Independence Models
[chapter]
2006
Lecture Notes in Computer Science
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We evaluate the classification performance of the symmetric causal independence models learned with the presented EM algorithm. ...
Causal independence modelling is a well-known method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. ...
The authors are grateful to Henk Boot and Babs Taal for the provided non-Hodgkin's lymphoma data. We would also like to thank Jiří Vomlel for sharing his code and insights. ...
doi:10.1007/11871842_25
fatcat:a2stdgqipbfyjj2fq3ph4rggey
Learning symmetric causal independence models
2008
Machine Learning
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The presented EM algorithm allows us to assess the practical usefulness of symmetric causal independence models. ...
In this paper, we study the problem of learning the parameters in these models, further referred to as symmetric causal independence models. ...
Acknowledgements The authors are grateful to Henk Boot and Babs Taal for providing the non-Hodgkin's lymphoma data, Gytis Karčiauskas for the preprocessed Reuters data and Jiří Vomlel for sharing his code ...
doi:10.1007/s10994-007-5041-7
fatcat:boyegxqbmje6dfxecrkur24jla
Multi-resolution classification of urban areas using hierarchical symmetric Markov mesh models
2017
2017 Joint Urban Remote Sensing Event (JURSE)
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In this paper we investigate a new hierarchical method for high resolution remotely sensed image classification. ...
The proposed approach integrates an explicit hierarchical graphbased classifier, which uses a quad-tree structure to model multiscale interactions, and a symmetric Markov mesh random field to deal with ...
ACKNOWLEDGMENT The authors wish to thank the French Space Agency (Centre National des Etudes Spatiales, CNES) for providing the data used in the experiments and for partial financial support. ...
doi:10.1109/jurse.2017.7924567
dblp:conf/jurse/HedhliMSZ17
fatcat:dwgbeasi2rhxbmsqo6evxzh3ei
Implementation of Human Cognitive Bias on Naïve Bayes
2016
Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)
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We propose a human-cognition inspired classification model based on Naïve Bayes. ...
In this paper, to investigate the mechanism that realizes the higher performance of classification, we further tested our model and its modified variant. ...
Hiroshi Sato of National Defense Academy of Japan and his students for their encouraging supports and comments. ...
doi:10.4108/eai.3-12-2015.2262494
dblp:journals/eetct/TaniguchiST16
fatcat:avysir36fzbhlhmdmeex2olueu
Predicting carcinoid heart disease with the noisy-threshold classifier
2007
Artificial Intelligence in Medicine
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Results: The noisy-threshold classifier showed the best classification accuracy of 72% correctly classified cases, although differences were significant only for logistic regression and C4.5. ...
An area under the ROC curve of 0.66 was attained for the noisy-threshold classifier, and equaled that of the physician's decision-rule. ...
We would like to thank the anonymous reviewers for their valuable comments. ...
doi:10.1016/j.artmed.2006.09.003
pmid:17098402
fatcat:qxspkhiidrahfeie2u5j4ysu74
A probabilistic framework for image information fusion with an application to mammographic analysis
2012
Medical Image Analysis
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independence models. ...
In this paper, we propose a novel method, that advances the state of the art of fusing image information from different views, based on a special class of probabilistic graphical models, called causal ...
comparison with the causal independence model in this study. ...
doi:10.1016/j.media.2012.01.003
pmid:22326491
fatcat:e7tilx4c3zdfhjevc6gptkptna
Semantic Features Prediction for Pulmonary Nodule Diagnosis based on Online Streaming Feature Selection
2019
IEEE Access
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In this paper, we exploit the causal discovery based on the streaming feature algorithm and causal discovery with symmetrical uncertainty based on the streaming feature algorithm. ...
INDEX TERMS Causal structure learning, online streaming feature selection, pulmonary nodule, semantic features selection. ...
After extracting the computational features, for the causal discovery based on the streaming feature (CD-SF) algorithm and causal discovery with symmetrical uncertainty based on the streaming feature ( ...
doi:10.1109/access.2019.2903682
fatcat:nj74etenv5hrzjmtvou25n6yrq
Eeg-based emotion recognition with brain network using independent components analysis and granger causality
2013
2013 International Conference on Computer Medical Applications (ICCMA)
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In this paper, a causal connectivity brain network (CCBN) was firstly constructed based on multivariate autoregressive (MVAR) modeling, independent component analysis (ICA) and partial directed coherence ...
a promising non-invasive approach for studying the model of affective computing. ...
Lei Xu of Southwest University for hosting this research and providing valuable comments. This work was financed by National Natural Science Foundation of China (Grant No.61170136 and No.61070077). ...
doi:10.1109/iccma.2013.6506157
fatcat:r56phaswozcuxn4ftqav2igdou
Conditional distribution variability measures for causality detection
[article]
2016
arXiv
pre-print
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In this paper we derive variability measures for the conditional probability distributions of a pair of random variables, and we study its application in the inference of causal-effect relationships. ...
The developed model obtains an AUC score of 0.82 on the final test database and ranked second in the challenge. ...
A binary model for estimating the direction (class 1 versus class -1) and a binary model for independence classification (class 0 versus the rest). ...
arXiv:1601.06680v1
fatcat:wg7ifbklmrar7hgdbqdxrg6yna
Seq2VAR: Multivariate Time Series Representation with Relational Neural Networks and Linear Autoregressive Model
[chapter]
2020
Lecture Notes in Computer Science
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The parameters of VAR models can be used as MTS feature representation. Yet, VAR cannot generalize on new samples, hence independent VAR models must be trained to represent different MTS. ...
We propose to associate a relational neural network to a VAR generative model to form an encoder-decoder of MTS. The model is denoted Seq2VAR for Sequenceto-VAR. ...
Test set is devided in train and test subset for the classification task. ...
doi:10.1007/978-3-030-39098-3_10
fatcat:zxyqzc7ujbcptltumr3icl72ma
Improved Spatio-temporal Salient Feature Detection for Action Recognition
2011
Procedings of the British Machine Vision Conference 2011
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A novel anisotropic temporal filter for better spatio-temporal feature detection is developed. The effect of symmetry and causality of the video filtering is investigated. ...
Existing methods use the same filter for both time and space and hence, perform an isotropic temporal filtering. ...
Acknowledgment GEOIDE (Geomatics for Informed Decision), a Network for Centers of Excellence supported by the NSERC Canada, is thanked for the financial support of this project. ...
doi:10.5244/c.25.100
dblp:conf/bmvc/ShabaniCZ11
fatcat:qq45lyubnbgetkcdtx6ekomb4e
Joint Classification of Multiresolution and Multisensor Data Using a Multiscale Markov Mesh Model
2019
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
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In this paper, the problem of the classification of multiresolution and multisensor remotely sensed data is addressed by proposing a multiscale Markov mesh model. ...
The proposed model allows taking benefit of strong analytical properties, most remarkably causality, which make it possible to apply time-efficient non-iterative inference algorithms. ...
To remove this drawback, the approach in [6] is adopted, in which a symmetric, corner-independent, isotropic formulation is defined and incorporated within the proposed hierarchical model. ...
doi:10.1109/igarss.2019.8898060
dblp:conf/igarss/MontaldoFHMZS19
fatcat:5qnos44q5bdcbhsct4nlgxxnp4
Causal Probabilistic Modelling for Two-View Mammographic Analysis
[chapter]
2009
Lecture Notes in Computer Science
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In this paper, we investigate a specific class of Bayesian networks-causal independence models-for exploring the dependencies between two breast image views. ...
Probabilistic modelling based on Bayesian networks is among the suitable tools, as it allows for the formalization of the uncertainty about parameters, models, and predictions in a statistical manner, ...
This work has been funded by the Netherlands Organization for Scientific Research under BRICKS/FOCUS grant number 642.066.605. ...
doi:10.1007/978-3-642-02976-9_56
fatcat:fdjcv7nstvcmvmg6dlm2daqs5q
The Mantel-Haenszel Procedure Revisited: Models and Generalizations
2013
PLoS ONE
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For situations where X and Y can be treated symmetrically we propose and explore the multinomial logistic model. ...
The idea is to replace the observed cell entries in strata of the Mantel-Haenszel procedure by subject specific classification probabilities for the four possible values of (X,Y) predicted by a suitable ...
It underlies the search for potentially causal relationships in observational research. ...
doi:10.1371/journal.pone.0058327
pmid:23516463
pmcid:PMC3596394
fatcat:cl4aiv7qzje2bmedoalrngxk2e
A CAUSAL HIERARCHICAL MARKOV FRAMEWORK FOR THE CLASSIFICATION OF MULTIRESOLUTION AND MULTISENSOR REMOTE SENSING IMAGES
2020
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Firstly, we prove the causality of the overall proposed model, a particularly advantageous property in terms of computational cost of the inference. ...
Secondly, we prove the expression of the marginal posterior mode criterion for inference on the proposed framework. ...
., and Google Crisis Response for providing the COSMO-SkyMed, QuickBird, and GeoEye-1 images used for experiments, respectively. ...
doi:10.5194/isprs-annals-v-3-2020-269-2020
fatcat:f32qfki74vcb7jfhi3aprj5fyy
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