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1
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
Analysis of classifiers’ robustness to adversarial perturbations
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Analysis of classifiers’ robustness to adversarial perturbations

Machine learning, 2018-03, Vol.107 (3), p.481-508 [Peer Reviewed Journal]

The Author(s) 2017 ;Machine Learning is a copyright of Springer, (2017). All Rights Reserved. ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-017-5663-3

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3
Clinicopathological and molecular characterisation of 'multiple-classifier' endometrial carcinomas
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Clinicopathological and molecular characterisation of 'multiple-classifier' endometrial carcinomas

The Journal of pathology, 2020-03, Vol.250 (3), p.312 [Peer Reviewed Journal]

ISSN: 1096-9896 ;EISSN: 1096-9896 ;DOI: 10.1002/path.5373

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4
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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5
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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6
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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7
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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8
Tag N’ Train: a technique to train improved classifiers on unlabeled data
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Tag N’ Train: a technique to train improved classifiers on unlabeled data

The journal of high energy physics, 2021-01, Vol.2021 (1), p.1-21, Article 153 [Peer Reviewed Journal]

The Author(s) 2021 ;The Author(s) 2021. This work is published under CC-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: 1029-8479 ;EISSN: 1029-8479 ;DOI: 10.1007/JHEP01(2021)153

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9
One-class classifiers
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One-class classifiers

Journal of chemometrics, 2011-05, Vol.25 (5), p.225-246 [Peer Reviewed Journal]

Copyright © 2011 John Wiley & Sons, Ltd. ;2015 INIST-CNRS ;ISSN: 0886-9383 ;ISSN: 1099-128X ;EISSN: 1099-128X ;DOI: 10.1002/cem.1397 ;CODEN: JOCHEU

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10
Nearest neighbors distance ratio open-set classifier
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Nearest neighbors distance ratio open-set classifier

Machine learning, 2017-03, Vol.106 (3), p.359-386 [Peer Reviewed Journal]

The Author(s) 2016 ;Machine Learning is a copyright of Springer, 2017. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-016-5610-8

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11
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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12
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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13
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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14
Classifier chains for multi-label classification
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Classifier chains for multi-label classification

Machine learning, 2011-12, Vol.85 (3), p.333-359 [Peer Reviewed Journal]

The Author(s) 2011 ;2015 INIST-CNRS ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-011-5256-5

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15
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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16
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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17
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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18
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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19
Machine learning classifiers and fMRI: A tutorial overview
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Article
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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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20
A geometric framework for multiclass ensemble classifiers
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A geometric framework for multiclass ensemble classifiers

Machine learning, 2023-12, Vol.112 (12), p.4929-4958 [Peer Reviewed Journal]

The Author(s) 2023 ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-023-06406-w

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