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EM Algorithm for Symmetric Causal Independence Models [chapter]

Rasa Jurgelenaite, Tom Heskes
2006 Lecture Notes in Computer Science  
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

Rasa Jurgelenaite, Tom Heskes
2008 Machine Learning  
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

Ihsen Hedhli, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia
2017 2017 Joint Urban Remote Sensing Event (JURSE)  
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

Hidetaka Taniguchi, Tomohiro Shirakawa, Tatsuji Takahashi
2016 Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)  
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

Marcel A.J. van Gerven, Rasa Jurgelenaite, Babs G. Taal, Tom Heskes, Peter J.F. Lucas
2007 Artificial Intelligence in Medicine  
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

Marina Velikova, Peter J.F. Lucas, Maurice Samulski, Nico Karssemeijer
2012 Medical Image Analysis  
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

Jing Yang, Na Li, Shuai Fang, Kui Yu, Yu Chen
2019 IEEE Access  
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

Chen Dongwei, Wu Fang, Wang Zhen, Li Haifang, Chen Junjie
2013 2013 International Conference on Computer Medical Applications (ICCMA)  
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]

José A. R. Fonollosa
2016 arXiv   pre-print
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]

Edouard Pineau, Sébastien Razakarivony, Thomas Bonald
2020 Lecture Notes in Computer Science  
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

Amir-Hossein Shabani, David Clausi, John S. Zelek
2011 Procedings of the British Machine Vision Conference 2011  
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

Alessandro Montaldo, Luca Fronda, Ihsen Hedhli, Gabriele Moser, Josiane Zerubia, Sebastiano B. Serpico
2019 IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium  
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]

Marina Velikova, Maurice Samulski, Peter J. F. Lucas, Nico Karssemeijer
2009 Lecture Notes in Computer Science  
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

Vaclav Fidler, Nico Nagelkerke, Niko Speybroeck
2013 PLoS ONE  
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

A. Montaldo, L. Fronda, I. Hedhli, G. Moser, S. B. Serpico, J. Zerubia
2020 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
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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