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PCA Feature Extraction for Change Detection in Multidimensional Unlabeled Data

IEEE transaction on neural networks and learning systems, 2014-01, Vol.25 (1), p.69-80

2014

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Combining Pattern Classifiers: Methods and Algorithms, 2nd Edition

Ludmila I. Kuncheva

2014

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Combining Pattern Classifiers: Methods and Algorithms

Kuncheva, Ludmila I

Second edition., 2014

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Change Detection in Streaming Multivariate Data Using Likelihood Detectors

IEEE transactions on knowledge and data engineering, 2013-05, Vol.25 (5), p.1175-1180

Kuncheva, L. I.

2013

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A Bound on Kappa-Error Diagrams for Analysis of Classifier Ensembles

IEEE transactions on knowledge and data engineering, 2013-03, Vol.25 (3), p.494-501

Kuncheva, L. I.

2013

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On feature selection protocols for very low-sample-size data

Pattern recognition, 2018-09, Vol.81, p.660-673

2018

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On the window size for classification in changing environments

Intelligent data analysis, 2009-01, Vol.13 (6), p.861-872

2009

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Classifier Ensembles for Changing Environments

Lecture notes in computer science, 2004, p.1-15

2004

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Diversity techniques improve the performance of the best imbalance learning ensembles

Information sciences, 2015-12, Vol.325, p.98-117

2015

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Using control charts for on-line video summarisation

MATEC Web of Conferences, 2019, Vol.277, p.1012

2019

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Rotation Forest: A New Classifier Ensemble Method

IEEE transactions on pattern analysis and machine intelligence, 2006-10, Vol.28 (10), p.1619-1630

2006

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A weighted voting framework for classifiers ensembles

Knowledge and information systems, 2014-02, Vol.38 (2), p.259-275

2014

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Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy

Machine learning, 2003-05, Vol.51 (2), p.181-207

2003

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A theoretical study on six classifier fusion strategies

IEEE transactions on pattern analysis and machine intelligence, 2002-02, Vol.24 (2), p.281-286

Kuncheva, L.I.

2002

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An experimental evaluation of mixup regression forests

Expert systems with applications, 2020-08, Vol.151, p.113376, Article 113376

2020

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Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization

IEEE transactions on pattern analysis and machine intelligence, 2006-11, Vol.28 (11), p.1798-1808

2006

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Random Balance ensembles for multiclass imbalance learning

Knowledge-based systems, 2020-04, Vol.193, p.105434, Article 105434

2020

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Classification and comparison of on-line video summarisation methods

Machine vision and applications, 2019-04, Vol.30 (3), p.507-518

2019

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Restricted Set Classification with prior probabilities: A case study on chessboard recognition

Pattern recognition letters, 2018-08, Vol.111, p.36-42

2018

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Random Subspace Ensembles for fMRI Classification

IEEE transactions on medical imaging, 2010-02, Vol.29 (2), p.531-542

2010

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Decision templates for multiple classifier fusion: an experimental comparison

Pattern recognition, 2001-02, Vol.34 (2), p.299-314

2001

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Instance selection improves geometric mean accuracy: a study on imbalanced data classification

Progress in artificial intelligence, 2019-06, Vol.8 (2), p.215-228

2019

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Classifier Ensembles with a Random Linear Oracle

IEEE transactions on knowledge and data engineering, 2007-04, Vol.19 (4), p.500-508

2007

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Classifier ensembles for fMRI data analysis: an experiment

Magnetic resonance imaging, 2010-05, Vol.28 (4), p.583-593

2010

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Switching between selection and fusion in combining classifiers: an experiment

IEEE transactions on cybernetics, 2002-04, Vol.32 (2), p.146-156

Kuncheva, L.I.

2002

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PCA Feature Extraction for Change Detection in Multidimensional Unlabeled Data

Combining Pattern Classifiers: Methods and Algorithms, 2nd Edition

Combining Pattern Classifiers: Methods and Algorithms

Change Detection in Streaming Multivariate Data Using Likelihood Detectors

A Bound on Kappa-Error Diagrams for Analysis of Classifier Ensembles

On feature selection protocols for very low-sample-size data

On the window size for classification in changing environments

Classifier Ensembles for Changing Environments

Diversity techniques improve the performance of the best imbalance learning ensembles

Using control charts for on-line video summarisation

Rotation Forest: A New Classifier Ensemble Method

A weighted voting framework for classifiers ensembles

Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy

A theoretical study on six classifier fusion strategies

An experimental evaluation of mixup regression forests

Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization

Random Balance ensembles for multiclass imbalance learning

Classification and comparison of on-line video summarisation methods

Restricted Set Classification with prior probabilities: A case study on chessboard recognition

Random Subspace Ensembles for fMRI Classification

Decision templates for multiple classifier fusion: an experimental comparison

Instance selection improves geometric mean accuracy: a study on imbalanced data classification

Classifier Ensembles with a Random Linear Oracle

Classifier ensembles for fMRI data analysis: an experiment

Switching between selection and fusion in combining classifiers: an experiment

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