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1
Ensemble-based classifiers
Ensemble-based classifiers
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Ensemble-based classifiers

The Artificial intelligence review, 2010-02, Vol.33 (1-2), p.1-39 [Peer Reviewed Journal]

Springer Science+Business Media B.V. 2009 ;Springer Science+Business Media B.V. 2010 ;ISSN: 0269-2821 ;EISSN: 1573-7462 ;DOI: 10.1007/s10462-009-9124-7

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2
Prediction of heart disease and classifiers’ sensitivity analysis
Prediction of heart disease and classifiers’ sensitivity analysis
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Prediction of heart disease and classifiers’ sensitivity analysis

BMC bioinformatics, 2020-07, Vol.21 (1), p.1-278, Article 278 [Peer Reviewed Journal]

COPYRIGHT 2020 BioMed Central Ltd. ;2020. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2020 ;ISSN: 1471-2105 ;EISSN: 1471-2105 ;DOI: 10.1186/s12859-020-03626-y ;PMID: 32615980

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3
Quantum ensembles of quantum classifiers
Quantum ensembles of quantum classifiers
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Quantum ensembles of quantum classifiers

Scientific reports, 2018-02, Vol.8 (1), p.2772-12, Article 2772 [Peer Reviewed Journal]

Copyright Nature Publishing Group Feb 2018 ;The Author(s) 2018 ;ISSN: 2045-2322 ;EISSN: 2045-2322 ;DOI: 10.1038/s41598-018-20403-3 ;PMID: 29426855

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4
A performance comparison of eight commercially available automatic classifiers for facial affect recognition
A performance comparison of eight commercially available automatic classifiers for facial affect recognition
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A performance comparison of eight commercially available automatic classifiers for facial affect recognition

PloS one, 2020-04, Vol.15 (4), p.e0231968-e0231968 [Peer Reviewed Journal]

COPYRIGHT 2020 Public Library of Science ;COPYRIGHT 2020 Public Library of Science ;2020 Dupré et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2020 Dupré et al 2020 Dupré et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0231968 ;PMID: 32330178

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5
Comprehensive benchmarking and ensemble approaches for metagenomic classifiers
Comprehensive benchmarking and ensemble approaches for metagenomic classifiers
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Comprehensive benchmarking and ensemble approaches for metagenomic classifiers

Genome Biology, 2017-09, Vol.18 (1), p.182-19, Article 182 [Peer Reviewed Journal]

2017. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s). 2017 ;ISSN: 1474-760X ;ISSN: 1474-7596 ;EISSN: 1474-760X ;DOI: 10.1186/s13059-017-1299-7 ;PMID: 28934964

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6
Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis
Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis
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Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis

Proceedings of the National Academy of Sciences - PNAS, 2020-06, Vol.117 (23), p.12592-12594 [Peer Reviewed Journal]

Copyright © 2020 the Author(s). Published by PNAS. ;Copyright National Academy of Sciences Jun 9, 2020 ;Copyright © 2020 the Author(s). Published by PNAS. 2020 ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.1919012117 ;PMID: 32457147

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7
Domain Adaptation for Statistical Classifiers
Domain Adaptation for Statistical Classifiers
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Domain Adaptation for Statistical Classifiers

The Journal of artificial intelligence research, 2006-01, Vol.26, p.101-126 [Peer Reviewed Journal]

2006. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at https://www.jair.org/index.php/jair/about ;ISSN: 1076-9757 ;EISSN: 1943-5037 ;DOI: 10.1613/jair.1872

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8
Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers
Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers
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Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers

NeuroImage (Orlando, Fla.), 2014-04, Vol.90, p.449-468 [Peer Reviewed Journal]

2014 ;2015 INIST-CNRS ;Copyright © 2014. Published by Elsevier Inc. ;Copyright Elsevier Limited Apr 15, 2014 ;ISSN: 1053-8119 ;EISSN: 1095-9572 ;DOI: 10.1016/j.neuroimage.2013.11.046 ;PMID: 24389422

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9
Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric
Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric
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Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric

PloS one, 2017-06, Vol.12 (6), p.e0177678-e0177678 [Peer Reviewed Journal]

COPYRIGHT 2017 Public Library of Science ;COPYRIGHT 2017 Public Library of Science ;2017 Boughorbel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2017 Boughorbel et al 2017 Boughorbel et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0177678 ;PMID: 28574989

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10
Differentiation of supratentorial single brain metastasis and glioblastoma by using peri-enhancing oedema region–derived radiomic features and multiple classifiers
Differentiation of supratentorial single brain metastasis and glioblastoma by using peri-enhancing oedema region–derived radiomic features and multiple classifiers
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Differentiation of supratentorial single brain metastasis and glioblastoma by using peri-enhancing oedema region–derived radiomic features and multiple classifiers

European radiology, 2020-05, Vol.30 (5), p.3015-3022 [Peer Reviewed Journal]

European Society of Radiology 2019 ;European Society of Radiology 2019. ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-019-06460-w ;PMID: 32006166

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11
Class-imbalanced classifiers for high-dimensional data
Class-imbalanced classifiers for high-dimensional data
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Class-imbalanced classifiers for high-dimensional data

Briefings in bioinformatics, 2013-01, Vol.14 (1), p.13-26 [Peer Reviewed Journal]

ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbs006 ;PMID: 22408190

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12
Machine learning classifiers and fMRI: A tutorial overview
Machine learning classifiers and fMRI: A tutorial overview
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Machine learning classifiers and fMRI: A tutorial overview

NeuroImage (Orlando, Fla.), 2009-03, Vol.45 (1), p.S199-S209 [Peer Reviewed Journal]

2008 Elsevier Inc. ;Copyright Elsevier Limited Mar 1, 2009 ;ISSN: 1053-8119 ;EISSN: 1095-9572 ;DOI: 10.1016/j.neuroimage.2008.11.007 ;PMID: 19070668

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13
Feature-based classifiers for somatic mutation detection in tumour-normal paired sequencing data
Feature-based classifiers for somatic mutation detection in tumour-normal paired sequencing data
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Feature-based classifiers for somatic mutation detection in tumour-normal paired sequencing data

Bioinformatics, 2012-01, Vol.28 (2), p.167-175 [Peer Reviewed Journal]

The Author(s) 2011. Published by Oxford University Press. 2011 ;2015 INIST-CNRS ;ISSN: 1367-4803 ;EISSN: 1460-2059 ;EISSN: 1367-4811 ;DOI: 10.1093/bioinformatics/btr629 ;PMID: 22084253

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14
Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning
Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning
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Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning

International journal of computer vision, 2021-06, Vol.129 (6), p.1930-1953 [Peer Reviewed Journal]

Springer Science+Business Media, LLC, part of Springer Nature 2021 ;COPYRIGHT 2021 Springer ;Springer Science+Business Media, LLC, part of Springer Nature 2021. ;ISSN: 0920-5691 ;EISSN: 1573-1405 ;DOI: 10.1007/s11263-020-01381-4

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15
Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study
Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study
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Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study

PloS one, 2015-12, Vol.10 (12), p.e0145118-e0145118 [Peer Reviewed Journal]

COPYRIGHT 2015 Public Library of Science ;COPYRIGHT 2015 Public Library of Science ;2015 Maier et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2015 Maier et al 2015 Maier et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0145118 ;PMID: 26672989

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16
Semi-supervised self-training for decision tree classifiers
Semi-supervised self-training for decision tree classifiers
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Article 		Add to My Research

Semi-supervised self-training for decision tree classifiers

International journal of machine learning and cybernetics, 2017-02, Vol.8 (1), p.355-370 [Peer Reviewed Journal]

The Author(s) 2015 ;ISSN: 1868-8071 ;EISSN: 1868-808X ;DOI: 10.1007/s13042-015-0328-7

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17
Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water
Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water
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Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water

Remote sensing (Basel, Switzerland), 2019-06, Vol.11 (11), p.1351 [Peer Reviewed Journal]

2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs11111351

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18
Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin
Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin
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Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin

Microbiome, 2018-05, Vol.6 (1), p.90-90, Article 90 [Peer Reviewed Journal]

COPYRIGHT 2018 BioMed Central Ltd. ;The Author(s). 2018 ;ISSN: 2049-2618 ;EISSN: 2049-2618 ;DOI: 10.1186/s40168-018-0470-z ;PMID: 29773078

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19
Fully automated stroke tissue estimation using random forest classifiers (FASTER)
Fully automated stroke tissue estimation using random forest classifiers (FASTER)
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Fully automated stroke tissue estimation using random forest classifiers (FASTER)

Journal of cerebral blood flow and metabolism, 2017-08, Vol.37 (8), p.2728-2741 [Peer Reviewed Journal]

The Author(s) 2016 ;The Author(s) 2016 2016 International Society for Cerebral Blood Flow and Metabolism ;ISSN: 0271-678X ;EISSN: 1559-7016 ;DOI: 10.1177/0271678X16674221 ;PMID: 27798267

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20
ICLabel: An automated electroencephalographic independent component classifier, dataset, and website
ICLabel: An automated electroencephalographic independent component classifier, dataset, and website
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Article 		Add to My Research

ICLabel: An automated electroencephalographic independent component classifier, dataset, and website

NeuroImage (Orlando, Fla.), 2019-09, Vol.198, p.181-197 [Peer Reviewed Journal]

2019 Elsevier Inc. ;Copyright © 2019 Elsevier Inc. All rights reserved. ;2019. Elsevier Inc. ;ISSN: 1053-8119 ;EISSN: 1095-9572 ;DOI: 10.1016/j.neuroimage.2019.05.026 ;PMID: 31103785

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