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Agreement Rate Initialized Maximum Likelihood Estimator for Ensemble Classifier Aggregation and Its Application in Brain-Computer Interface [article]

Dongrui Wu, Vernon J. Lawhern, Stephen Gordon, Brent J. Lance, Chin-Teng Lin
2018 arXiv   pre-print
This paper proposes an agreement rate initialized maximum likelihood estimator (ARIMLE) to optimally fuse the base classifiers.  ...  Extensive experiments on visually evoked potential classification in a brain-computer interface application show that ARIMLE outperforms majority voting, and also achieves better or comparable performance  ...  This paper proposes a new classifier combination approach, agreement rate initialized maximum likelihood estimator (ARIMLE), to aggregate the base classifiers.  ... 
arXiv:1805.04740v1 fatcat:5ufawh7gsjanxgmzjvo2qyllmi

Multiple Relevant Feature Ensemble Selection Based on Multilayer Co-Evolutionary Consensus MapReduce

Weiping Ding, Chin-Teng Lin, Witold Pedrycz
2018 IEEE Transactions on Cybernetics  
It is critical to extract knowledge and build modplexes, which calls for the need for mechanisms to detect some els from big data.  ...  These classification prediction for large-scale and complex brain data in useless features often diminish the learning process associterms of efficiency and feasibility. ated with classification algorithms  ...  ACKNOWLEDGMENT The authors would like to express their sincere appreciation to the anonymous reviewers for their insightful comments, which greatly improved the quality of this paper.  ... 
doi:10.1109/tcyb.2018.2859342 pmid:30130243 fatcat:lk7dgvlfhjcj3exex3ddlgss7q

Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface

Nicholas R. Waytowich, Vernon J. Lawhern, Addison W. Bohannon, Kenneth R. Ball, Brent J. Lance
2016 Frontiers in Neuroscience  
Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however  ...  In this paper, we present an unsupervised transfer method (spectral transfer using information geometry, STIG), which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers  ...  Army Research Laboratory administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and USARL.  ... 
doi:10.3389/fnins.2016.00430 pmid:27713685 pmcid:PMC5032911 fatcat:m4wiwdfp65do3mpgcvohsttnei

Bayesian networks in neuroscience: a survey

Concha Bielza, Pedro Larrañaga
2014 Frontiers in Computational Neuroscience  
(C) The dynamic BN unfolded in time for three time slices.  ...  the brain aspects to be studied.  ...  from EPFL) and the European Union Seventh Framework Programme (FP7/2007(FP7/ -2013 under grant agreement no. 604102 (Human Brain Project).  ... 
doi:10.3389/fncom.2014.00131 pmid:25360109 pmcid:PMC4199264 fatcat:2ip7hztt4fexdj5cw4a2gpmgbu

A Ternary Brain-Computer Interface Based on Single-Trial Readiness Potentials of Self-initiated Fine Movements: A Diversified Classification Scheme

Elias Abou Zeid, Alborz Rezazadeh Sereshkeh, Benjamin Schultz, Tom Chau
2017 Frontiers in Human Neuroscience  
In recent years, the readiness potential (RP), a type of pre-movement neural activity, has been investigated for asynchronous electroencephalogram (EEG)-based brain-computer interfaces (BCIs).  ...  This BCI classifies amongst an idle state, a left hand and a right hand self-initiated fine movement.  ...  ., and Bagheri, N. (2013). Mutliple classifier system for EEG signal classification with application to brain computer interfaces. Neural Comput.  ... 
doi:10.3389/fnhum.2017.00254 pmid:28596725 pmcid:PMC5443161 fatcat:crrghmfacbeylbirzsxh2ostyi

Adaptive frequency-based modeling of whole-brain oscillations: Predicting regional vulnerability and hazardousness rates

Neda Kaboodvand, Martijn P. van den Heuvel, Peter Fransson
2019 Network Neuroscience  
We present a new approach to map the effects of vulnerability in brain networks and introduce a measure of regional hazardousness based on mapping of the degree of divergence in a feature space.  ...  Whole-brain computational modeling based on structural connectivity has shown great promise in successfully simulating fMRI BOLD signals with temporal coactivation patterns that are highly similar to empirical  ...  The resultant distribution of composite scores was used to estimate the maximum value of the likelihood function for the model.  ... 
doi:10.1162/netn_a_00104 pmid:31637340 pmcid:PMC6779267 fatcat:hjfcdn2vszdh3mamuvmfiehauq

Forecasting methods for cloud hosted resources, a comparison

H. A. Engelbrecht, M van Greunen
2015 2015 11th International Conference on Network and Service Management (CNSM)  
under-estimation rates), Overload Likelihood Ratio and Overloaded State Likelihood Ratio.  ...  This produces a maximum likelihood estimation of the initial distribution over all states. Let us look at the series produced after digitising the example data we have been using thus far.  ...  The Yule-Walker equations are used to estimate the Auto-regression, AR(p) of order p, model parameters φ i .  ... 
doi:10.1109/cnsm.2015.7367335 dblp:conf/cnsm/EngelbrechtG15 fatcat:dqmmritvuzaztmyamj4ex6t33u

Deconstructing Commercial Wearable Technology: Contributions toward Accurate and Free-Living Monitoring of Sleep

Lauren E. Rentz, Hana K. Ulman, Scott M. Galster
2021 Sensors  
and contributing factors for overall device success.  ...  Thus, this review provides context surrounding the complex hardware and software developed by wearable device companies in their attempts to estimate sleep-related phenomena, and outlines considerations  ...  Informed Consent Statement: Not Applicable. Data Availability Statement: Not Applicable. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21155071 fatcat:vzkczyuc6bdzrn5f745ha6odr4

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes

Lena Davidson, Mary Regina Boland
2021 Briefings in Bioinformatics  
We identified 127 distinct studies from our queries that were relevant to our topic and included in the review.  ...  Efforts to translate AI into clinical care include clinical decision support systems (n = 24) and mobile health applications (n = 9).  ...  It is unrealistic to assume that AI applications in health are ethically neutral.  ... 
doi:10.1093/bib/bbaa369 pmid:33406530 pmcid:PMC8424395 fatcat:rgsf4pdmbvdcdmcwz4zf3hwaca

Modelling the dynamic pattern of surface area in basketball and its effects on team performance

Rodolfo Metulini, Marica Manisera, Paola Zuccolotto
2018 Journal of Quantitative Analysis in Sports (JQAS)  
analysis of a game with respect to surface areas dynamics and (ii) to investigate its influence on the points made by both the team and the opponent.  ...  Then, we perform descriptive analyses in order to highlight associations between regimes and relevant game variables.  ...  Theoretically, the characterisation of the nonparametric maximum likelihood estimate is studied and the algorithm is guaranteed to converge to the unique maximum likelihood estimate.  ... 
doi:10.1515/jqas-2018-0041 fatcat:b3qwsi7tqjg2vdo7gbjtiorv6m

Histopathological Image Analysis: A Review

M.N. Gurcan, L.E. Boucheron, A. Can, A. Madabhushi, N.M. Rajpoot, B. Yener
2009 IEEE Reviews in Biomedical Engineering  
This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe.  ...  Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques.  ...  Learning ensembles or multiple classifier systems are methods for improving classification accuracy through aggregation of several similar classifiers' predictions TABLE III THE AVERAGE ACCURACY RESULTS  ... 
doi:10.1109/rbme.2009.2034865 pmid:20671804 pmcid:PMC2910932 fatcat:a6sm4iy5gffbhlc23dtlp7xe2q

A Holistic Strategy for Classification of Sleep Stages with EEG

Sunil Kumar Prabhakar, Harikumar Rajaguru, Semin Ryu, In cheol Jeong, Dong-Ok Won
2022 Sensors  
In this work, a holistic strategy named as clustering and dimensionality reduction with feature extraction cum selection for classification along with deep learning (CDFCD) is proposed for the classification  ...  As it is a very hectic task to perform, automated sleep stage classification systems were developed in the past, and advancements are being made consistently by researchers.  ...  The EEG, apart from sleep analysis, helps in many other important applications, such as motor imagery classification [9] , visual feedback classification [10] , subject independent brain-computer interface  ... 
doi:10.3390/s22093557 pmid:35591246 pmcid:PMC9103466 fatcat:odonx7ek7vbrjaosjdwef4vetm

The cloudUPDRS app: A medical device for the clinical assessment of Parkinson's Disease

C. Stamate, G.D. Magoulas, S. Kueppers, E. Nomikou, I. Daskalopoulos, A. Jha, J.S. Pons, J. Rothwell, M.U. Luchini, T. Moussouri, M. Iannone, G. Roussos
2018 Pervasive and Mobile Computing  
The app follows closely Part III of the Unified Parkinson's Disease Rating Scale which is the most commonly used protocol in the clinical study of PD; can be used by patients and their carers at home or  ...  in the community unsupervised; and, requires the user to perform a sequence of iterated movements which are recorded by the phone sensors.  ...  In all cases, acceleration is recorded along three axes in m/s 2 at the maximum supported sampling rate (at least 50 Hz) and timestamped at maximum resolution (typically microseconds).  ... 
doi:10.1016/j.pmcj.2017.12.005 fatcat:zmoucmxpsjcr5mjvcnidiqpphq

Reconstructing networks [article]

Giulio Cimini, Rossana Mastrandrea, Tiziano Squartini
2021 arXiv   pre-print
Given the extent of the subject, we shall focus on the inference methods rooted in statistical physics and information theory.  ...  This Element provides an overview of the ideas, methods and techniques to deal with this problem and that together define the field of network reconstruction.  ...  In this case, computing the joint posterior distribution for µ and ν returns constraints implying that the inferred error rates are bounded by the maximum and minimum inferred connection probabilities  ... 
arXiv:2012.02677v2 fatcat:2rwmpvmrqnbafcmkannssavyly

Multi-Source Multi-Domain Data Fusion for Cyberattack Detection in Power Systems

Abhijeet Sahu, Zeyu Mao, Patrick Wlazlo, Hao Huang, Katherine Davis, Ana Goulart, Saman Zonouz.
2021 IEEE Access  
It is an ensemble based classifier in which a diverse collection of classifiers (decision trees) VOLUME 4, 2021 are constructed by incorporating randomness in tree construction.  ...  DATA FUSION SOFTWARE APPLICATION A desktop application as a data fusion framework is developed for data aggregation, feature extraction, transformation, fusion, and learning purposes as illustrated in  ... 
doi:10.1109/access.2021.3106873 fatcat:4aemwsqnunhvpavu426fzkvhg4
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