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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
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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3
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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4
Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers
Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers
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Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers

IEEE access, 2020, Vol.8, p.76516-76531 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2020.2989857 ;CODEN: IAECCG

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5
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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6
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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7
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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8
Learning likelihood ratios with neural network classifiers
Learning likelihood ratios with neural network classifiers
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Learning likelihood ratios with neural network classifiers

The journal of high energy physics, 2024-02, Vol.2024 (2), p.136-41 [Peer Reviewed Journal]

The Author(s) 2024 ;The Author(s) 2024. This work is published 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. ;EISSN: 1029-8479 ;DOI: 10.1007/JHEP02(2024)136

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9
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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10
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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11
Test Input Prioritization for Machine Learning Classifiers
Test Input Prioritization for Machine Learning Classifiers
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Test Input Prioritization for Machine Learning Classifiers

IEEE transactions on software engineering, 2024-03, Vol.50 (3), p.413-442 [Peer Reviewed Journal]

ISSN: 0098-5589 ;DOI: 10.1109/TSE.2024.3350019 ;CODEN: IESEDJ

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12
SPOCC: Scalable POssibilistic Classifier Combination - toward robust aggregation of classifiers
SPOCC: Scalable POssibilistic Classifier Combination - toward robust aggregation of classifiers
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SPOCC: Scalable POssibilistic Classifier Combination - toward robust aggregation of classifiers

Expert systems with applications, 2020-07, Vol.150 [Peer Reviewed Journal]

Attribution - NonCommercial ;ISSN: 0957-4174 ;EISSN: 1873-6793 ;DOI: 10.1016/j.eswa.2020.113332

Digital Resources/Online E-Resources

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13
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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14
ICLabel: An automated electroencephalographic independent component classifier, dataset, and website
ICLabel: An automated electroencephalographic independent component classifier, dataset, and website
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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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15
Ensemble of a subset of kNN classifiers
Ensemble of a subset of kNN classifiers
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Ensemble of a subset of kNN classifiers

Advances in data analysis and classification, 2018, Vol.12 (4), p.827-840 [Peer Reviewed Journal]

The Author(s) 2016 ;Advances in Data Analysis and Classification is a copyright of Springer, (2016). All Rights Reserved. © 2016. This work is published 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. ;ISSN: 1862-5347 ;EISSN: 1862-5355 ;DOI: 10.1007/s11634-015-0227-5 ;PMID: 30931011

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16
Sex‐related patterns in the electroencephalogram and their relevance in machine learning classifiers
Sex‐related patterns in the electroencephalogram and their relevance in machine learning classifiers
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Sex‐related patterns in the electroencephalogram and their relevance in machine learning classifiers

Human brain mapping, 2023-10, Vol.44 (14), p.4848-4858 [Peer Reviewed Journal]

2023 The Authors. published by Wiley Periodicals LLC. ;2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. ;2023. This article is published 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. ;ISSN: 1065-9471 ;EISSN: 1097-0193 ;DOI: 10.1002/hbm.26417 ;PMID: 37461294

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17
Predicting Blood⁻Brain Barrier Permeability of Marine-Derived Kinase Inhibitors Using Ensemble Classifiers Reveals Potential Hits for Neurodegenerative Disorders
Predicting Blood⁻Brain Barrier Permeability of Marine-Derived Kinase Inhibitors Using Ensemble Classifiers Reveals Potential Hits for Neurodegenerative Disorders
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Predicting Blood⁻Brain Barrier Permeability of Marine-Derived Kinase Inhibitors Using Ensemble Classifiers Reveals Potential Hits for Neurodegenerative Disorders

Marine drugs, 2019-01, Vol.17 (2), p.81 [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: 1660-3397 ;EISSN: 1660-3397 ;DOI: 10.3390/md17020081 ;PMID: 30699889

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18
Privacy preserving distributed learning classifiers – Sequential learning with small sets of data
Privacy preserving distributed learning classifiers – Sequential learning with small sets of data
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Privacy preserving distributed learning classifiers – Sequential learning with small sets of data

Computers in biology and medicine, 2021-09, Vol.136, p.104716-104716, Article 104716 [Peer Reviewed Journal]

2021 The Author(s) ;2021. The Author(s) ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2021.104716

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19
An Efficient Classifier for Alzheimer's Disease Genes Identification
An Efficient Classifier for Alzheimer's Disease Genes Identification
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An Efficient Classifier for Alzheimer's Disease Genes Identification

Molecules (Basel, Switzerland), 2018-11, Vol.23 (12), p.3140 [Peer Reviewed Journal]

2018 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. ;2018 by the authors. 2018 ;ISSN: 1420-3049 ;EISSN: 1420-3049 ;DOI: 10.3390/molecules23123140 ;PMID: 30501121

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20
Deep Learning Classifiers for Automated Detection of Gonioscopic Angle Closure Based on Anterior Segment OCT Images
Deep Learning Classifiers for Automated Detection of Gonioscopic Angle Closure Based on Anterior Segment OCT Images
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Deep Learning Classifiers for Automated Detection of Gonioscopic Angle Closure Based on Anterior Segment OCT Images

American journal of ophthalmology, 2019-12, Vol.208, p.273-280 [Peer Reviewed Journal]

2019 Elsevier Inc. ;Copyright © 2019 Elsevier Inc. All rights reserved. ;2019. Elsevier Inc. ;ISSN: 0002-9394 ;EISSN: 1879-1891 ;DOI: 10.1016/j.ajo.2019.08.004 ;PMID: 31445003

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