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1
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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2
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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3
Explaining classifiers by constructing familiar concepts
Explaining classifiers by constructing familiar concepts
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Explaining classifiers by constructing familiar concepts

Machine learning, 2023-11, Vol.112 (11), p.4167-4200 [Peer Reviewed Journal]

The Author(s) 2022 ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-022-06157-0

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4
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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5
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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6
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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7
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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8
Graphical assessment of tests and classifiers
Graphical assessment of tests and classifiers
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Graphical assessment of tests and classifiers

Nature methods, 2021-08, Vol.18 (8), p.840-842 [Peer Reviewed Journal]

COPYRIGHT 2021 Nature Publishing Group ;Springer Nature America, Inc. 2021. ;ISSN: 1548-7091 ;EISSN: 1548-7105 ;DOI: 10.1038/s41592-021-01232-1

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9
Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy
Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy
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Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy

Applied and Environmental Microbiology, 2007-08, Vol.73 (16), p.5261-5267 [Peer Reviewed Journal]

2007 INIST-CNRS ;Copyright American Society for Microbiology Aug 2007 ;Copyright © 2007, American Society for Microbiology 2007 ;ISSN: 0099-2240 ;EISSN: 1098-5336 ;DOI: 10.1128/AEM.00062-07 ;PMID: 17586664 ;CODEN: AEMIDF

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10
Variational quantum classifiers through the lens of the Hessian
Variational quantum classifiers through the lens of the Hessian
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Variational quantum classifiers through the lens of the Hessian

PloS one, 2022-01, Vol.17 (1), p.e0262346-e0262346 [Peer Reviewed Journal]

COPYRIGHT 2022 Public Library of Science ;2022 Sen 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. ;2022 Sen et al 2022 Sen et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0262346 ;PMID: 35051206

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11
Types of minority class examples and their influence on learning classifiers from imbalanced data
Types of minority class examples and their influence on learning classifiers from imbalanced data
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Types of minority class examples and their influence on learning classifiers from imbalanced data

Journal of intelligent information systems, 2016-06, Vol.46 (3), p.563-597 [Peer Reviewed Journal]

The Author(s) 2015 ;Springer Science+Business Media New York 2016 ;ISSN: 0925-9902 ;EISSN: 1573-7675 ;DOI: 10.1007/s10844-015-0368-1

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12
Exact p-values for pairwise comparison of Friedman rank sums, with application to comparing classifiers
Exact p-values for pairwise comparison of Friedman rank sums, with application to comparing classifiers
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Exact p-values for pairwise comparison of Friedman rank sums, with application to comparing classifiers

BMC bioinformatics, 2017-01, Vol.18 (1), p.68-68, Article 68 [Peer Reviewed Journal]

COPYRIGHT 2017 BioMed Central Ltd. ;Copyright BioMed Central 2017 ;The Author(s). 2017 ;ISSN: 1471-2105 ;EISSN: 1471-2105 ;DOI: 10.1186/s12859-017-1486-2 ;PMID: 28122501

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13
Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation
Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation
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Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation

Remote sensing (Basel, Switzerland), 2021-11, Vol.13 (21), p.4405 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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/rs13214405

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14
A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China
A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China
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A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China

Geomatics, natural hazards and risk, 2017-12, Vol.8 (2), p.1955-1977 [Peer Reviewed Journal]

2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. 2017 ;2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License 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. ;ISSN: 1947-5705 ;EISSN: 1947-5713 ;DOI: 10.1080/19475705.2017.1401560

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15
Classifier-based constraint acquisition
Classifier-based constraint acquisition
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Classifier-based constraint acquisition

Annals of mathematics and artificial intelligence, 2021-07, Vol.89 (7), p.655-674 [Peer Reviewed Journal]

The Author(s) 2021 ;ISSN: 1012-2443 ;EISSN: 1573-7470 ;DOI: 10.1007/s10472-021-09736-4

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16
Early Prediction of Alzheimer's Disease Using Null Longitudinal Model-Based Classifiers
Early Prediction of Alzheimer's Disease Using Null Longitudinal Model-Based Classifiers
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Early Prediction of Alzheimer's Disease Using Null Longitudinal Model-Based Classifiers

PloS one, 2017-01, Vol.12 (1), p.e0168011-e0168011 [Peer Reviewed Journal]

COPYRIGHT 2017 Public Library of Science ;COPYRIGHT 2017 Public Library of Science ;2017 Gavidia-Bovadilla 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. ;info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/3.0/es/ ;2017 Gavidia-Bovadilla et al 2017 Gavidia-Bovadilla et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0168011 ;PMID: 28045907

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17
Multi-Level Comparison of Machine Learning Classifiers and Their Performance Metrics
Multi-Level Comparison of Machine Learning Classifiers and Their Performance Metrics
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Multi-Level Comparison of Machine Learning Classifiers and Their Performance Metrics

Molecules (Basel, Switzerland), 2019-08, Vol.24 (15), p.2811 [Peer Reviewed Journal]

2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2019 by the authors. 2019 ;ISSN: 1420-3049 ;EISSN: 1420-3049 ;DOI: 10.3390/molecules24152811 ;PMID: 31374986

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18
How to evaluate sentiment classifiers for Twitter time-ordered data?
How to evaluate sentiment classifiers for Twitter time-ordered data?
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How to evaluate sentiment classifiers for Twitter time-ordered data?

PloS one, 2018-03, Vol.13 (3), p.e0194317-e0194317 [Peer Reviewed Journal]

COPYRIGHT 2018 Public Library of Science ;COPYRIGHT 2018 Public Library of Science ;2018 Mozetič 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. ;2018 Mozetič et al 2018 Mozetič et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0194317 ;PMID: 29534112

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19
Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting
Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting
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Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting

Sensors (Basel, Switzerland), 2024-05, Vol.24 (9), p.2863 [Peer Reviewed Journal]

COPYRIGHT 2024 MDPI AG ;2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s24092863 ;PMID: 38732969

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20
Exploration of mRNAs and miRNA classifiers for various ATLL cancer subtypes using machine learning
Exploration of mRNAs and miRNA classifiers for various ATLL cancer subtypes using machine learning
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Exploration of mRNAs and miRNA classifiers for various ATLL cancer subtypes using machine learning

BMC cancer, 2022-04, Vol.22 (1), p.433-433, Article 433 [Peer Reviewed Journal]

2022. The Author(s). ;COPYRIGHT 2022 BioMed Central Ltd. ;2022. 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) 2022 ;ISSN: 1471-2407 ;EISSN: 1471-2407 ;DOI: 10.1186/s12885-022-09540-1 ;PMID: 35449091

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