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1
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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2
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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3
A systematic comparison of supervised classifiers
A systematic comparison of supervised classifiers
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A systematic comparison of supervised classifiers

PloS one, 2014-04, Vol.9 (4), p.e94137-e94137 [Peer Reviewed Journal]

COPYRIGHT 2014 Public Library of Science ;COPYRIGHT 2014 Public Library of Science ;2014 Amancio 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. ;2014 Amancio et al 2014 Amancio et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0094137 ;PMID: 24763312

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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.182-182, 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
Stable reliability diagrams for probabilistic classifiers
Stable reliability diagrams for probabilistic classifiers
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Stable reliability diagrams for probabilistic classifiers

Proceedings of the National Academy of Sciences - PNAS, 2021-02, Vol.118 (8) [Peer Reviewed Journal]

Copyright © 2021 the Author(s). Published by PNAS. ;Copyright © 2021 the Author(s). Published by PNAS. 2021 ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.2016191118 ;PMID: 33597296

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8
Humans as intuitive classifiers
Humans as intuitive classifiers
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Humans as intuitive classifiers

Frontiers in psychology, 2023-01, Vol.13, p.1041737-1041737 [Peer Reviewed Journal]

Copyright © 2023 Erev and Marx. ;Copyright © 2023 Erev and Marx. 2023 Erev and Marx ;ISSN: 1664-1078 ;EISSN: 1664-1078 ;DOI: 10.3389/fpsyg.2022.1041737 ;PMID: 36710808

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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
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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11
Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer
Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer
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Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer

Frontiers in oncology, 2015-12, Vol.5, p.272-272 [Peer Reviewed Journal]

COPYRIGHT 2015 Frontiers Research Foundation ;Copyright © 2015 Parmar, Grossmann, Rietveld, Rietbergen, Lambin and Aerts. 2015 Parmar, Grossmann, Rietveld, Rietbergen, Lambin and Aerts ;ISSN: 2234-943X ;EISSN: 2234-943X ;DOI: 10.3389/fonc.2015.00272 ;PMID: 26697407

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12
The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets
The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets
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The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets

PloS one, 2015-03, Vol.10 (3), p.e0118432-e0118432 [Peer Reviewed Journal]

COPYRIGHT 2015 Public Library of Science ;COPYRIGHT 2015 Public Library of Science ;2015 Saito, Rehmsmeier. 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 Saito, Rehmsmeier 2015 Saito, Rehmsmeier ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0118432 ;PMID: 25738806

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13
Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery
Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery
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Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery

Sensors (Basel, Switzerland), 2017-12, Vol.18 (1), p.18 [Peer Reviewed Journal]

2018. 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. ;2017 by the authors. 2017 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s18010018 ;PMID: 29271909

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14
MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers
MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers
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MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

Sensors (Basel, Switzerland), 2021-03, Vol.21 (6), p.2222 [Peer Reviewed Journal]

2021 by the authors. 2021 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s21062222 ;PMID: 33810176

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15
Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology
Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology
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Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology

Frontiers in oncology, 2016-03, Vol.6, p.71-71 [Peer Reviewed Journal]

Copyright © 2016 Wu, Parmar, Grossmann, Quackenbush, Lambin, Bussink, Mak and Aerts. 2016 Wu, Parmar, Grossmann, Quackenbush, Lambin, Bussink, Mak and Aerts ;ISSN: 2234-943X ;EISSN: 2234-943X ;DOI: 10.3389/fonc.2016.00071 ;PMID: 27064691

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16
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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17
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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18
Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers
Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers
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Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers

Sensors (Basel, Switzerland), 2020-03, Vol.20 (7), p.1884 [Peer Reviewed Journal]

2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2020 by the authors. 2020 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s20071884 ;PMID: 32231167

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19
Deconstructing Cross-Entropy for Probabilistic Binary Classifiers
Deconstructing Cross-Entropy for Probabilistic Binary Classifiers
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Deconstructing Cross-Entropy for Probabilistic Binary Classifiers

Entropy (Basel, Switzerland), 2018-03, Vol.20 (3), p.208 [Peer Reviewed Journal]

Copyright MDPI AG 2018 ;2018 by the authors. 2018 ;ISSN: 1099-4300 ;EISSN: 1099-4300 ;DOI: 10.3390/e20030208 ;PMID: 33265299

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20
Ovarian Cancer-Specific BRCA -like Copy-Number Aberration Classifiers Detect Mutations Associated with Homologous Recombination Deficiency in the AGO-TR1 Trial
Ovarian Cancer-Specific BRCA -like Copy-Number Aberration Classifiers Detect Mutations Associated with Homologous Recombination Deficiency in the AGO-TR1 Trial
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Ovarian Cancer-Specific BRCA -like Copy-Number Aberration Classifiers Detect Mutations Associated with Homologous Recombination Deficiency in the AGO-TR1 Trial

Clinical cancer research, 2021-12, Vol.27 (23), p.6559-6569 [Peer Reviewed Journal]

2021 The Authors; Published by the American Association for Cancer Research. ;2021 The Authors; Published by the American Association for Cancer Research 2021 American Association for Cancer Research ;ISSN: 1078-0432 ;EISSN: 1557-3265 ;DOI: 10.1158/1078-0432.CCR-21-1673 ;PMID: 34593530

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