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1
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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2
Classifiers Combination Techniques: A Comprehensive Review
Classifiers Combination Techniques: A Comprehensive Review
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Classifiers Combination Techniques: A Comprehensive Review

IEEE access, 2018-01, Vol.6, p.19626-19639 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2018.2813079 ;CODEN: IAECCG

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3
A hybrid hierarchical framework for classification of breast density using digitized film screen mammograms
A hybrid hierarchical framework for classification of breast density using digitized film screen mammograms
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A hybrid hierarchical framework for classification of breast density using digitized film screen mammograms

Multimedia tools and applications, 2017-09, Vol.76 (18), p.18789-18813 [Peer Reviewed Journal]

Springer Science+Business Media New York 2017 ;Multimedia Tools and Applications is a copyright of Springer, (2017). All Rights Reserved. ;ISSN: 1380-7501 ;EISSN: 1573-7721 ;DOI: 10.1007/s11042-016-4340-z

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4
An effective combining classifier approach using tree algorithms for network intrusion detection
An effective combining classifier approach using tree algorithms for network intrusion detection
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An effective combining classifier approach using tree algorithms for network intrusion detection

Neural computing & applications, 2017-12, Vol.28 (Suppl 1), p.1051-1058 [Peer Reviewed Journal]

The Natural Computing Applications Forum 2016 ;Copyright Springer Science & Business Media 2017 ;ISSN: 0941-0643 ;EISSN: 1433-3058 ;DOI: 10.1007/s00521-016-2418-1

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5
Dementia classification using MR imaging and clinical data with voting based machine learning models
Dementia classification using MR imaging and clinical data with voting based machine learning models
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Dementia classification using MR imaging and clinical data with voting based machine learning models

Multimedia tools and applications, 2022-07, Vol.81 (18), p.25971-25992 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 ;The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. ;ISSN: 1380-7501 ;EISSN: 1573-7721 ;DOI: 10.1007/s11042-022-12754-x

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6
An Empirical Analysis of Machine Learning Algorithms for Crime Prediction Using Stacked Generalization: An Ensemble Approach
An Empirical Analysis of Machine Learning Algorithms for Crime Prediction Using Stacked Generalization: An Ensemble Approach
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An Empirical Analysis of Machine Learning Algorithms for Crime Prediction Using Stacked Generalization: An Ensemble Approach

IEEE access, 2021, Vol.9, p.67488-67500 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2021.3075140 ;CODEN: IAECCG

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7
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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8
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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9
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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10
A Random Forest classifier-based approach in the detection of abnormalities in the retina
A Random Forest classifier-based approach in the detection of abnormalities in the retina
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A Random Forest classifier-based approach in the detection of abnormalities in the retina

Medical & biological engineering & computing, 2019, Vol.57 (1), p.193-203 [Peer Reviewed Journal]

International Federation for Medical and Biological Engineering 2018 ;Medical & Biological Engineering & Computing is a copyright of Springer, (2018). All Rights Reserved. ;ISSN: 0140-0118 ;EISSN: 1741-0444 ;DOI: 10.1007/s11517-018-1878-0 ;PMID: 30076537

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11
Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier
Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier
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Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier

Computers in biology and medicine, 2020-08, Vol.123, p.103899, Article 103899 [Peer Reviewed Journal]

2020 Elsevier Ltd ;Copyright © 2020 Elsevier Ltd. All rights reserved. ;2020. Elsevier Ltd ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2020.103899 ;PMID: 32768046

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12
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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13
Phishing Detection System through Hybrid Machine Learning Based on URL
Phishing Detection System through Hybrid Machine Learning Based on URL
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Phishing Detection System through Hybrid Machine Learning Based on URL

IEEE access, 2023-01, Vol.11, p.1-1 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2023.3252366 ;CODEN: IAECCG

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14
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

Digital Resources/Online E-Resources

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15
Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM
Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM
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Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM

Applied intelligence (Dordrecht, Netherlands), 2021-10, Vol.51 (10), p.7094-7108 [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: 0924-669X ;EISSN: 1573-7497 ;DOI: 10.1007/s10489-021-02205-9

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16
Interpretable time series classification using linear models and multi-resolution multi-domain symbolic representations
Interpretable time series classification using linear models and multi-resolution multi-domain symbolic representations
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Interpretable time series classification using linear models and multi-resolution multi-domain symbolic representations

Data mining and knowledge discovery, 2019-07, Vol.33 (4), p.1183-1222 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2019 ;Data Mining and Knowledge Discovery is a copyright of Springer, (2019). All Rights Reserved. ;ISSN: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-019-00633-3

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17
Experimental Comparisons of Multi-class Classifiers
Experimental Comparisons of Multi-class Classifiers
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Experimental Comparisons of Multi-class Classifiers

Informatica (Ljubljana), 2015-03, Vol.39 (1), p.71-71 [Peer Reviewed Journal]

Copyright Slovenian Society Informatika / Slovensko drustvo Informatika Mar 2015 ;ISSN: 0350-5596 ;EISSN: 1854-3871

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18
Automated detection of heart valve diseases using chirplet transform and multiclass composite classifier with PCG signals
Automated detection of heart valve diseases using chirplet transform and multiclass composite classifier with PCG signals
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Automated detection of heart valve diseases using chirplet transform and multiclass composite classifier with PCG signals

Computers in biology and medicine, 2020-03, Vol.118, p.103632-103632, Article 103632 [Peer Reviewed Journal]

2020 Elsevier Ltd ;Copyright © 2020 Elsevier Ltd. All rights reserved. ;2020. Elsevier Ltd ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2020.103632 ;PMID: 32174311

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19
ECG Signal Preprocessing and SVM Classifier-Based Abnormality Detection in Remote Healthcare Applications
ECG Signal Preprocessing and SVM Classifier-Based Abnormality Detection in Remote Healthcare Applications
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ECG Signal Preprocessing and SVM Classifier-Based Abnormality Detection in Remote Healthcare Applications

IEEE access, 2018-01, Vol.6, p.9767-9773 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2018.2794346 ;CODEN: IAECCG

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20
Probing Classifiers: Promises, Shortcomings, and Advances
Probing Classifiers: Promises, Shortcomings, and Advances
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Probing Classifiers: Promises, Shortcomings, and Advances

Computational linguistics - Association for Computational Linguistics, 2022-04, Vol.48 (1), p.207-219 [Peer Reviewed Journal]

2022. This work is published under https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0891-2017 ;EISSN: 1530-9312 ;DOI: 10.1162/coli_a_00422

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