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Dynamic Bayesian Combination of Multiple Imperfect Classifiers [article]

Edwin Simpson, Stephen Roberts, Ioannis Psorakis, Arfon Smith
2012 arXiv   pre-print
Classifier combination methods need to make best use of the outputs of multiple, imperfect classifiers to enable higher accuracy classifications.  ...  Finally we present a dynamic Bayesian classifier combination approach and investigate the changes in base classifier performance over time.  ...  Discussion In this paper we present a very computationally efficient, variational Bayesian, approach to imperfect multiple classifier combination.  ... 
arXiv:1206.1831v1 fatcat:izkywacgozcofegcrbpmj5djyq

Dynamic Bayesian Combination of Multiple Imperfect Classifiers [chapter]

Edwin Simpson, Stephen Roberts, Ioannis Psorakis, Arfon Smith
2013 Studies in Computational Intelligence  
Classifier combination methods need to make best use of the outputs of multiple, imperfect classifiers to enable higher accuracy classifications.  ...  Finally we present a dynamic Bayesian classifier combination approach and investigate the changes in base classifier performance over time.  ...  Discussion In this paper we present a very computationally efficient, variational Bayesian, approach to imperfect multiple classifier combination.  ... 
doi:10.1007/978-3-642-36406-8_1 fatcat:qw4eavqggvbu3ouikiamcs6ooi

Dynamically weighted ensemble classification for non-stationary EEG processing

Sidath Ravindra Liyanage, Cuntai Guan, Haihong Zhang, Kai Keng Ang, JianXin Xu, Tong Heng Lee
2013 Journal of Neural Engineering  
The decisions from these multiple classifiers are dynamically combined based on the distances of the cluster centres to each test data sample being classified.  ...  This paper proposes a novel Dynamically Weighted Ensemble Classification (DWEC) framework to address the non-stationarity. An ensemble of multiple classifiers are trained on clustered features.  ...  Recent advances include a unifying framework for learning linear combiners for classifier ensembles [15] and Bayesian combination of multiple imperfect classifiers proposed by Simpson et al in [16]  ... 
doi:10.1088/1741-2560/10/3/036007 pmid:23574821 fatcat:mrygve3tcjdf3n5qf3i436zvea

Game Theory for Network Security

Xiannuan Liang, Yang Xiao
2013 IEEE Communications Surveys and Tutorials  
of subcategories.  ...  In addition to the introduction to the state of the art, we discuss the limitations of those game theoretic approaches and propose future research directions.  ...  The dynamic Bayesian game in [18] includes multiple stages, and each of the stages is a static game.  ... 
doi:10.1109/surv.2012.062612.00056 fatcat:wn7l3y2sgvaqvaneotfzhaxzoy

Novel classifier fusion approahces for fault diagnosis in automotive systems

Kihoon Choi, Satnam Singh, Anuradha Kodali, Krishna R. Pattipati, John W. Sheppard, Setu Madhavi Namburu, Shunsuke Chigusa, Danil V. Prokhorov, Liu Qiao
2007 IEEE Autotestcon Proceedings  
Specifically, we develop three novel classifier fusion approaches: class-specific Bayesian fusion, joint optimization of fusion center and of individual classifiers, and dynamic fusion.  ...  The results demonstrate that dynamic fusion and joint optimization, and class-specific Bayesian fusion outperform traditional fusion approaches.  ...  ACKNOWLEDGEMENT This work was supported by Toyota Technical Center at the University of Connecticut under agreement AG030699-02.  ... 
doi:10.1109/autest.2007.4374227 fatcat:46lc32pujneg5nlakgk7ugnr54

Multibiometric Systems: Overview, Case Studies, and Open Issues [chapter]

Arun Ross, Norman Poh
2009 Advances in Pattern Recognition  
Multibiometric systems combine the information presented by multiple biometric sensors, algorithms, samples, units, or traits in order to establish the identity of an individual.  ...  Information fusion refers to the reconciliation of evidence presented by multiple sources of information in order to generate a decision.  ...  [30] (logistic regression classifier) and [45] (Bayesian classifier).  ... 
doi:10.1007/978-1-84882-385-3_11 fatcat:xipdozpwjbhzvexgftr2cdtfrq

Dynamic fusion of classifiers for fault diagnosis

Satnam Singh, Kihoon Choi, Anuradha Kodali, Krishna R. Pattipati, Setu Madhavi Namburu, Shunsuke Chigusa, Danil V. Prokhorov, Liu Qiao
2007 2007 IEEE International Conference on Systems, Man and Cybernetics  
Here, we discuss dynamic fusion of classifiers which is a special case of the dynamic multiple fault diagnosis (DMFD) problem [1]-[3].  ...  The results demonstrate that an ensemble of classifiers, when fused over time, reduces the diagnostic error as compared to a single classifier and static fusion of classifiers trained over the entire batch  ...  The true states of classifiers are also hidden because the classifiers are imperfect.  ... 
doi:10.1109/icsmc.2007.4414167 dblp:conf/smc/SinghCKPNCPQ07 fatcat:kivld7nhfnex3pkaoy7wdhgmr4

Feature Tracking and Expression Recognition of Face Using Dynamic Bayesian Network
English

Sonali V.Hedaoo, M.D. Katkar, S.P. Khandait
2014 International Journal of Engineering Trends and Technoloy  
In proposed system, in contrast to the mainstream approaches, we are trying to build a probabilistic model based on the Dynamic Bayesian Network (DBN) to capture the facial interactions at different levels  ...  Due to the difficulty of obtaining controlled video sequences of standard facial expressions, many psychological and neurophysiologic studies of facial expression processing have used single image motivations  ...  Given the proposed model, all three levels of facial activities are recovered simultaneously through a probabilistic inference by systematically combining the measurements from multiple sources at different  ... 
doi:10.14445/22315381/ijett-v8p293 fatcat:jnt2mhufsrfnllw7b6dkzwx63u

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence: Navigating the absence of a gold standard

Sahar Saeed, Sheila F. O'Brien, Kento Abe, Qi-Long Yi, Bhavisha Rathod, Jenny Wang, Mahya Fazel-Zarandi, Ashleigh Tuite, David Fisman, Heidi Wood, Karen Colwill, Anne-Claude Gingras (+2 others)
2021 PLoS ONE  
Given the dynamic nature of this pandemic, Bayesian Latent Class Models can be used to correct for imperfect test characteristics and waning IgG antibody signals.  ...  Seroprevalence rates were compared using multiple composite reference standards and by a series of Bayesian Latent Class Models.  ...  In contrast to CRS which classifies individuals as either positive or negative, BLCA uses a likelihood-based approach from multiple imperfect assays to estimate test characteristics and prevalence.  ... 
doi:10.1371/journal.pone.0257743 pmid:34555095 fatcat:ucd6rcqj3banrhgru4ayepuoae

Object Recognition and Identification Using ESM Data [article]

E. Taghavi, D. Song, R. Tharmarasa, T. Kirubarajan, Anne-Claire Boury-Brisset, Bhashyam Balaji
2016 arXiv   pre-print
In typical surveillance systems multiple ESM sensors are usually deployed along with kinematic sensors like radar.  ...  Recognition and identification of unknown targets is a crucial task in surveillance and security systems.  ...  Imperfect nature of information can also be considered in the architecture.  ... 
arXiv:1607.01355v1 fatcat:rvznjlospvegvfwy5bdoynzzri

Multi-Sensor Data and Knowledge Fusion – A Proposal for a Terminology Definition [article]

Silvia Beddar-Wiesing, Maarten Bieshaar
2020 arXiv   pre-print
The focus in the first part of the paper at hand is on the clear definition of the terminology and the development of an appropriate ontology of the fusion components and the fusion level.  ...  However, a clear definition of the type of fusion is not always provided due to inconsistent literature.  ...  Decision Fusion The decision fusion of multiple classifiers may consist of the direct combination of decisions or the selection of one suitable classifier for a specific input area.  ... 
arXiv:2001.04171v1 fatcat:2wj5xe3bqrdf3bt7sn2em7vqne

Novel Classifier Fusion Approaches for Fault Diagnosis in Automotive Systems

Kihoon Choi, S. Singh, A. Kodali, K.R. Pattipati, J.W. Sheppard, S.M. Namburu, S. Chigusa, D.V. Prokhorov, Liu Qiao
2009 IEEE Transactions on Instrumentation and Measurement  
Specifically, we develop three novel classifier fusion approaches: class-specific Bayesian fusion, joint optimization of fusion center and of individual classifiers, and dynamic fusion.  ...  In this paper, we consider the problem of fusing classifier decisions to reduce diagnostic errors.  ...  The objective of classifier fusion is to reduce the diagnostic errors by combining the results of individual classifiers.  ... 
doi:10.1109/tim.2008.2004340 fatcat:qebotwkravc35bfs4fpgdnvy6a

A Survey of Game Theory as Applied to Network Security

Sankardas Roy, Charles Ellis, Sajjan Shiva, Dipankar Dasgupta, Vivek Shandilya, Qishi Wu
2010 2010 43rd Hawaii International Conference on System Sciences  
This paper surveys the existing game theoretic solutions which are designed to enhance network security and presents a taxonomy for classifying the proposed solutions.  ...  This taxonomy should provide the reader with a better understanding of game theoretic solutions to a variety of cyber security problems.  ...  Dynamic games A dynamic game can be either of complete or incomplete information. Moreover, a dynamic game may involve perfect or imperfect information.  ... 
doi:10.1109/hicss.2010.35 dblp:conf/hicss/RoyESDSW10 fatcat:hom3hguma5a4nji65wdcoiseoq

Abstracts of Recent PhDs

2008 Knowledge engineering review (Print)  
'environment' and a number of strategic tradeoffs.  ...  The variety of different auction rules, the restrictions in supply or demand, and the agents' combinatorial preferences for the different commodities, have led to the creation of a very complex multi-agent  ...  Furthermore, we present several novel Bayes-Nash equilibria for m th price multi-unit auctions with multiple possible closing times, one of which is chosen randomly, and therefore multiple rounds of bidding  ... 
doi:10.1017/s0269888908001331 fatcat:lil6wn52szbdjklbdqa3m4durq

Comparison of levels and fusion approaches for multimodal biometrics

S. Sujana, V. S. K. Reddy
2021 Indonesian Journal of Electrical Engineering and Computer Science  
In this paper we have discussed comprehensive representation on the system of multimodal biometric, various modes of undertakings, the significance of information fusion, a different section is allotted  ...  In today's world, most of the systems are unimodal biometrics having a lot of limitations to overcome those multimodal biometrics comes in to picture.  ...  Bayesian inference and dynamic Bayesian network come under generative, whereas neural networks and supportive vector machines are under Discriminative models.  ... 
doi:10.11591/ijeecs.v23.i2.pp791-801 fatcat:j64gws32kzfufa3kg2dfhpxjwi
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