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1
The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances
The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances
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The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances

Data mining and knowledge discovery, 2021-03, Vol.35 (2), p.401-449 [Peer Reviewed Journal]

The Author(s) 2020 ;The Author(s) 2020. ;The Author(s) 2020. 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: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-020-00727-3 ;PMID: 33679210

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2
nDNA-Prot: identification of DNA-binding proteins based on unbalanced classification
nDNA-Prot: identification of DNA-binding proteins based on unbalanced classification
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nDNA-Prot: identification of DNA-binding proteins based on unbalanced classification

BMC bioinformatics, 2014-09, Vol.15 (1), p.298-298, Article 298 [Peer Reviewed Journal]

COPYRIGHT 2014 BioMed Central Ltd. ;2014 Song et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. ;Song et al.; licensee BioMed Central Ltd. 2014 ;ISSN: 1471-2105 ;EISSN: 1471-2105 ;DOI: 10.1186/1471-2105-15-298 ;PMID: 25196432

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3
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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4
A new VIKOR-based in-sample-out-of-sample classifier with application in bankruptcy prediction
A new VIKOR-based in-sample-out-of-sample classifier with application in bankruptcy prediction
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A new VIKOR-based in-sample-out-of-sample classifier with application in bankruptcy prediction

Annals of operations research, 2021-01, Vol.296 (1-2), p.495-512 [Peer Reviewed Journal]

The Author(s) 2019 ;COPYRIGHT 2021 Springer ;Annals of Operations Research is a copyright of Springer, (2019). All Rights Reserved. © 2019. 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. ;The Author(s) 2019. This work is published 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: 0254-5330 ;EISSN: 1572-9338 ;DOI: 10.1007/s10479-019-03223-0

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5
Comparing the performance of meta-classifiers—a case study on selected imbalanced data sets relevant for prediction of liver toxicity
Comparing the performance of meta-classifiers—a case study on selected imbalanced data sets relevant for prediction of liver toxicity
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Comparing the performance of meta-classifiers—a case study on selected imbalanced data sets relevant for prediction of liver toxicity

Journal of computer-aided molecular design, 2018-05, Vol.32 (5), p.583-590 [Peer Reviewed Journal]

The Author(s) 2018 ;Journal of Computer-Aided Molecular Design is a copyright of Springer, (2018). All Rights Reserved. ;ISSN: 0920-654X ;EISSN: 1573-4951 ;DOI: 10.1007/s10822-018-0116-z ;PMID: 29626291

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6
Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits
Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits
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Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits

Nature communications, 2018-06, Vol.9 (1), p.2331-7, Article 2331 [Peer Reviewed Journal]

2018. 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. ;The Author(s) 2018 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-018-04482-4 ;PMID: 29899421

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7
A review of epileptic seizure detection using machine learning classifiers
A review of epileptic seizure detection using machine learning classifiers
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A review of epileptic seizure detection using machine learning classifiers

Brain informatics, 2020-05, Vol.7 (1), p.5-5, Article 5 [Peer Reviewed Journal]

The Author(s) 2020 ;COPYRIGHT 2020 Springer ;The Author(s) 2020. 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: 2198-4018 ;EISSN: 2198-4026 ;DOI: 10.1186/s40708-020-00105-1 ;PMID: 32451639

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8
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
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Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods

Machine learning, 2021-03, Vol.110 (3), p.457-506 [Peer Reviewed Journal]

The Author(s) 2021 ;The Author(s) 2021. 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: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-021-05946-3

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9
A comprehensive evaluation of ensemble learning for stock-market prediction
A comprehensive evaluation of ensemble learning for stock-market prediction
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A comprehensive evaluation of ensemble learning for stock-market prediction

Journal of big data, 2020-03, Vol.7 (1), p.1-40, Article 20 [Peer Reviewed Journal]

The Author(s) 2020 ;Journal of Big Data is a copyright of Springer, (2020). All Rights Reserved. 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: 2196-1115 ;EISSN: 2196-1115 ;DOI: 10.1186/s40537-020-00299-5

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10
PlasForest: a homology-based random forest classifier for plasmid detection in genomic datasets
PlasForest: a homology-based random forest classifier for plasmid detection in genomic datasets
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PlasForest: a homology-based random forest classifier for plasmid detection in genomic datasets

BMC bioinformatics, 2021-06, Vol.22 (1), p.1-349, Article 349 [Peer Reviewed Journal]

COPYRIGHT 2021 BioMed Central Ltd. ;2021. 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. ;Distributed under a Creative Commons Attribution 4.0 International License ;The Author(s) 2021 ;ISSN: 1471-2105 ;EISSN: 1471-2105 ;DOI: 10.1186/s12859-021-04270-w ;PMID: 34174810

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11
Optimization of sentiment analysis using machine learning classifiers
Optimization of sentiment analysis using machine learning classifiers
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Optimization of sentiment analysis using machine learning classifiers

Human-centric computing and information sciences, 2017-12, Vol.7 (1), p.1-12, Article 32 [Peer Reviewed Journal]

The Author(s) 2017 ;Human-centric Computing and Information Sciences is a copyright of Springer, (2017). All Rights Reserved. © 2017. 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: 2192-1962 ;EISSN: 2192-1962 ;DOI: 10.1186/s13673-017-0116-3

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12
A comparative analysis of gradient boosting algorithms
A comparative analysis of gradient boosting algorithms
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A comparative analysis of gradient boosting algorithms

The Artificial intelligence review, 2021-03, Vol.54 (3), p.1937-1967 [Peer Reviewed Journal]

Springer Nature B.V. 2020 ;COPYRIGHT 2021 Springer ;Springer Nature B.V. 2020. ;ISSN: 0269-2821 ;EISSN: 1573-7462 ;DOI: 10.1007/s10462-020-09896-5

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13
IoT-cloud based healthcare model for COVID-19 detection: an enhanced k-Nearest Neighbour classifier based approach
IoT-cloud based healthcare model for COVID-19 detection: an enhanced k-Nearest Neighbour classifier based approach
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IoT-cloud based healthcare model for COVID-19 detection: an enhanced k-Nearest Neighbour classifier based approach

Computing, 2023-04, Vol.105 (4), p.849-869 [Peer Reviewed Journal]

The Author(s) 2021 ;ISSN: 0010-485X ;EISSN: 1436-5057 ;DOI: 10.1007/s00607-021-00951-9

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14
Problem formulations and solvers in linear SVM: a review
Problem formulations and solvers in linear SVM: a review
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Problem formulations and solvers in linear SVM: a review

The Artificial intelligence review, 2019-08, Vol.52 (2), p.803-855 [Peer Reviewed Journal]

Springer Science+Business Media B.V., part of Springer Nature 2018 ;COPYRIGHT 2019 Springer ;Artificial Intelligence Review is a copyright of Springer, (2018). All Rights Reserved. ;ISSN: 0269-2821 ;EISSN: 1573-7462 ;DOI: 10.1007/s10462-018-9614-6

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15
Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images
Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images
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Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images

Cognitive computation, 2020-09, Vol.12 (5), p.1011-1023 [Peer Reviewed Journal]

The Author(s) 2020 ;ISSN: 1866-9956 ;EISSN: 1866-9964 ;DOI: 10.1007/s12559-020-09751-3

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16
On the need of preserving order of data when validating within-project defect classifiers
On the need of preserving order of data when validating within-project defect classifiers
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On the need of preserving order of data when validating within-project defect classifiers

Empirical software engineering : an international journal, 2020-11, Vol.25 (6), p.4805-4830 [Peer Reviewed Journal]

The Author(s) 2020. corrected publication 2020 ;The Author(s) 2020. corrected publication 2020. 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: 1382-3256 ;EISSN: 1573-7616 ;DOI: 10.1007/s10664-020-09868-x

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17
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances
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The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances

Data mining and knowledge discovery, 2017-05, Vol.31 (3), p.606-660 [Peer Reviewed Journal]

The Author(s) 2016 ;Data Mining and Knowledge Discovery is a copyright of Springer, 2017. ;ISSN: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-016-0483-9 ;PMID: 30930678

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18
Tree-based dynamic classifier chains
Tree-based dynamic classifier chains
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Tree-based dynamic classifier chains

Machine learning, 2023-11, Vol.112 (11), p.4129-4165 [Peer Reviewed Journal]

The Author(s) 2022 ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-022-06162-3

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19
The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation
The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation
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The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation

BioData mining, 2021-02, Vol.14 (1), p.13-13, Article 13 [Peer Reviewed Journal]

COPYRIGHT 2021 BioMed Central Ltd. ;2021. 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) 2021 ;ISSN: 1756-0381 ;EISSN: 1756-0381 ;DOI: 10.1186/s13040-021-00244-z ;PMID: 33541410

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20
On the robustness of randomized classifiers to adversarial examples
On the robustness of randomized classifiers to adversarial examples
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On the robustness of randomized classifiers to adversarial examples

Machine learning, 2022-09, Vol.111 (9), p.3425-3457 [Peer Reviewed Journal]

The Author(s) 2022 ;The Author(s) 2022. 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. ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-022-06216-6

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