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1
Two-Stage Hybrid Data Classifiers Based on SVM and kNN Algorithms
Two-Stage Hybrid Data Classifiers Based on SVM and kNN Algorithms
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Two-Stage Hybrid Data Classifiers Based on SVM and kNN Algorithms

Symmetry (Basel), 2021-04, Vol.13 (4), p.615 [Peer Reviewed Journal]

2021 by the author. 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: 2073-8994 ;EISSN: 2073-8994 ;DOI: 10.3390/sym13040615

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2
Nearest neighbors distance ratio open-set classifier
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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3
Machine learning classification can reduce false positives in structure-based virtual screening
Machine learning classification can reduce false positives in structure-based virtual screening
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Machine learning classification can reduce false positives in structure-based virtual screening

Proceedings of the National Academy of Sciences - PNAS, 2020-08, Vol.117 (31), p.18477-18488 [Peer Reviewed Journal]

Copyright National Academy of Sciences Aug 4, 2020 ;Copyright © 2020 the Author(s). Published by PNAS. 2020 ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.2000585117 ;PMID: 32669436

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4
TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System
TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System
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TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System

IEEE access, 2019, Vol.7, p.94497-94507 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2019.2928048 ;CODEN: IAECCG

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5
Prevalence of neural collapse during the terminal phase of deep learning training
Prevalence of neural collapse during the terminal phase of deep learning training
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Prevalence of neural collapse during the terminal phase of deep learning training

Proceedings of the National Academy of Sciences - PNAS, 2020-10, Vol.117 (40), p.24652-24663 [Peer Reviewed Journal]

Copyright National Academy of Sciences Oct 6, 2020 ;Copyright © 2020 the Author(s). Published by PNAS. 2020 ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.2015509117 ;PMID: 32958680

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6
Online Active Learning Ensemble Framework for Drifted Data Streams
Online Active Learning Ensemble Framework for Drifted Data Streams
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Online Active Learning Ensemble Framework for Drifted Data Streams

IEEE transaction on neural networks and learning systems, 2019-02, Vol.30 (2), p.486-498

ISSN: 2162-237X ;EISSN: 2162-2388 ;DOI: 10.1109/TNNLS.2018.2844332 ;CODEN: ITNNAL

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7
Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery
Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery
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Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery

Remote sensing (Basel, Switzerland), 2015-01, Vol.7 (1), p.153-168 [Peer Reviewed Journal]

Copyright MDPI AG 2015 ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs70100153

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8
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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9
Accuracy Improvement of Transformer Faults Diagnostic Based on DGA Data Using SVM-BA Classifier
Accuracy Improvement of Transformer Faults Diagnostic Based on DGA Data Using SVM-BA Classifier
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Accuracy Improvement of Transformer Faults Diagnostic Based on DGA Data Using SVM-BA Classifier

Energies (Basel), 2021-05, Vol.14 (10), p.2970 [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: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en14102970

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10
A Pedestrian Detection Method Based on Genetic Algorithm for Optimize XGBoost Training Parameters
A Pedestrian Detection Method Based on Genetic Algorithm for Optimize XGBoost Training Parameters
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A Pedestrian Detection Method Based on Genetic Algorithm for Optimize XGBoost Training Parameters

IEEE access, 2019, Vol.7, p.118310-118321 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2019.2936454 ;CODEN: IAECCG

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11
Improving AdaBoost-based Intrusion Detection System (IDS) Performance on CIC IDS 2017 Dataset
Improving AdaBoost-based Intrusion Detection System (IDS) Performance on CIC IDS 2017 Dataset
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Improving AdaBoost-based Intrusion Detection System (IDS) Performance on CIC IDS 2017 Dataset

Journal of physics. Conference series, 2019-03, Vol.1192 (1), p.12018 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2019. This work is published 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. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/1192/1/012018

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12
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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13
A Novel Diversity Measure and Classifier Selection Approach for Generating Ensemble Classifiers
A Novel Diversity Measure and Classifier Selection Approach for Generating Ensemble Classifiers
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A Novel Diversity Measure and Classifier Selection Approach for Generating Ensemble Classifiers

IEEE access, 2019, Vol.7, p.156360-156373 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2019.2949059 ;CODEN: IAECCG

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14
Designing an ensemble of classifiers for smartphone-based indoor localization irrespective of device configuration
Designing an ensemble of classifiers for smartphone-based indoor localization irrespective of device configuration
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Designing an ensemble of classifiers for smartphone-based indoor localization irrespective of device configuration

Multimedia tools and applications, 2021-05, Vol.80 (13), p.20501-20525 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 ;The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021. ;ISSN: 1380-7501 ;EISSN: 1573-7721 ;DOI: 10.1007/s11042-020-10456-w

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15
Prediction of oral hepatotoxic dose of natural products derived from traditional Chinese medicines based on SVM classifier and PBPK modeling
Prediction of oral hepatotoxic dose of natural products derived from traditional Chinese medicines based on SVM classifier and PBPK modeling
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Prediction of oral hepatotoxic dose of natural products derived from traditional Chinese medicines based on SVM classifier and PBPK modeling

Archives of toxicology, 2021-05, Vol.95 (5), p.1683-1701 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 ;The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. ;ISSN: 0340-5761 ;EISSN: 1432-0738 ;DOI: 10.1007/s00204-021-03023-1 ;PMID: 33713150

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16
BadNets: Evaluating Backdooring Attacks on Deep Neural Networks
BadNets: Evaluating Backdooring Attacks on Deep Neural Networks
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BadNets: Evaluating Backdooring Attacks on Deep Neural Networks

IEEE access, 2019, Vol.7, p.47230-47244 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2019.2909068 ;CODEN: IAECCG

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17
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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18
Efficient Medical Diagnosis of Human Heart Diseases Using Machine Learning Techniques With and Without GridSearchCV
Efficient Medical Diagnosis of Human Heart Diseases Using Machine Learning Techniques With and Without GridSearchCV
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Efficient Medical Diagnosis of Human Heart Diseases Using Machine Learning Techniques With and Without GridSearchCV

IEEE access, 2022, Vol.10, p.80151-80173 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2022.3165792 ;CODEN: IAECCG

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19
Loda: Lightweight on-line detector of anomalies
Loda: Lightweight on-line detector of anomalies
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Loda: Lightweight on-line detector of anomalies

Machine learning, 2016-02, Vol.102 (2), p.275-304 [Peer Reviewed Journal]

The Author(s) 2015 ;The Author(s) 2016 ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-015-5521-0

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20
Cerebral Infarction Classification Using the K-Nearest Neighbor and Naive Bayes Classifier
Cerebral Infarction Classification Using the K-Nearest Neighbor and Naive Bayes Classifier
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Cerebral Infarction Classification Using the K-Nearest Neighbor and Naive Bayes Classifier

Journal of physics. Conference series, 2021-02, Vol.1752 (1), p.12045 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2021. This work is published 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. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/1752/1/012045

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