Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification | Paper Citation Record

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This paper cites Learning from class-imbalanced data: Review of methods and applications.Expert Systems with Applications, 73:220–239, 2017.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Learning from class-imbalanced data: Review of methods and applications.Expert Systems with Applications, 73:220–239, 2017

Reference 1

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This paper cites A hybrid feature selection with ensemble classiﬁcation for imbalanced healthcare data: A case study for brain tumor diagnosis.IEEE Access, 4:9145– 9154, 2016.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification A hybrid feature selection with ensemble classiﬁcation for imbalanced healthcare data: A case study for brain tumor diagnosis.IEEE Access, 4:9145– 9154, 2016

Reference 2

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Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Unresolved cited work

Reference 3

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This paper cites Rosefw-rf: the winner algorithm for the ecbdl’14 big data competition: an extremely imbalanced big data bioinformatics problem.Knowledge-Based Systems, 87:69–79, 2015.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Rosefw-rf: the winner algorithm for the ecbdl’14 big data competition: an extremely imbalanced big data bioinformatics problem.Knowledge-Based Systems, 87:69–79, 2015

Reference 4

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Reference 5

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This paper cites A study on combining dynamic selection and data preprocessing for imbalance learning.Neurocomputing, 286:179–192, 2018.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification A study on combining dynamic selection and data preprocessing for imbalance learning.Neurocomputing, 286:179–192, 2018

Reference 6

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This paper cites Combining multiple algorithms in classiﬁer ensembles using generalized mixture functions.Neurocomputing, 313:402–414, 2018.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Combining multiple algorithms in classiﬁer ensembles using generalized mixture functions.Neurocomputing, 313:402–414, 2018

Reference 7

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This paper cites A framework for dynamic classiﬁer selection oriented by the classiﬁcation problem diﬃculty.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification A framework for dynamic classiﬁer selection oriented by the classiﬁcation problem diﬃculty

Reference 8

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This paper cites Bayesian reasoning and machine learning.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Bayesian reasoning and machine learning

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This paper cites Robust text-independent speaker identiﬁcation using gaussian mixture speaker models.IEEE transactions on speech and audio processing, 3(1):72–83, 1995.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Robust text-independent speaker identiﬁcation using gaussian mixture speaker models.IEEE transactions on speech and audio processing, 3(1):72–83, 1995

Reference 10

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This paper cites Learning characteristics of stochastic-gradient-descent algorithms: A general study, analysis, and critique.Signal processing, 6(2):113–133, 1984.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Learning characteristics of stochastic-gradient-descent algorithms: A general study, analysis, and critique.Signal processing, 6(2):113–133, 1984

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This paper cites Under- standing deep learning requires rethinking generalization.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Under- standing deep learning requires rethinking generalization

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This paper cites Deep learning in neural networks: An overview.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Deep learning in neural networks: An overview

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This paper cites Optimization methods for large-scale ma- chine learning.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Optimization methods for large-scale ma- chine learning

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This paper cites Xgboost: A scalable tree boosting system.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Xgboost: A scalable tree boosting system

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This paper cites Focal loss for dense object detection.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Focal loss for dense object detection

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This paper cites Imbalance-xgboost: Leveraging weighted and focal losses for binary label-imbalanced classiﬁcation with xgboost.Pattern Recognition Letters, 2020.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Imbalance-xgboost: Leveraging weighted and focal losses for binary label-imbalanced classiﬁcation with xgboost.Pattern Recognition Letters, 2020

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This paper cites Lightgbm: A highly eﬃcient gradient boosting decision tree.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Lightgbm: A highly eﬃcient gradient boosting decision tree

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This paper cites Handling data irregularities in classiﬁcation: Foundations, trends, and future challenges.Pattern Recognition, 81:674–693, 2018.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Handling data irregularities in classiﬁcation: Foundations, trends, and future challenges.Pattern Recognition, 81:674–693, 2018

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This paper cites Analysing the classiﬁcation of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches.Knowledge-based systems, 42:97–110, 2013.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Analysing the classiﬁcation of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches.Knowledge-based systems, 42:97–110, 2013

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This paper cites Learning from imbalanced data: open challenges and future directions.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Learning from imbalanced data: open challenges and future directions

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This paper cites Survey of resampling techniques for improving classification performance in unbalanced datasets.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Survey of resampling techniques for improving classification performance in unbalanced datasets

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This paper cites Cost-sensitive learning of deep feature representations from imbalanced data.IEEE transactions on neural networks and learning systems, 2017.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Cost-sensitive learning of deep feature representations from imbalanced data.IEEE transactions on neural networks and learning systems, 2017

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This paper cites Near-bayesian support vector machines for imbalanced data classiﬁcation with equal or unequal misclassiﬁcation costs.Neural Networks, 70:39–52, 2015.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Near-bayesian support vector machines for imbalanced data classiﬁcation with equal or unequal misclassiﬁcation costs.Neural Networks, 70:39–52, 2015

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This paper cites Class-speciﬁc extreme learning machine for handling binary class imbalance problem.Neural Networks, 105:206–217, 2018.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Class-speciﬁc extreme learning machine for handling binary class imbalance problem.Neural Networks, 105:206–217, 2018

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This paper cites Online sequential class-speciﬁc extreme learning machine for binary imbalanced learning.Neural Networks, 119:235–248, 2019.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Online sequential class-speciﬁc extreme learning machine for binary imbalanced learning.Neural Networks, 119:235–248, 2019

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This paper cites One-class versus binary classi- ﬁcation: Which and when? In Machine Learning and Applications (ICMLA), 2012 11th International Conference on, volume 2, pages 102–106.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification One-class versus binary classi- ﬁcation: Which and when? In Machine Learning and Applications (ICMLA), 2012 11th International Conference on, volume 2, pages 102–106

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Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Feature learning with a divergence-encouraging autoencoder for imbalanced data classiﬁcation.IEEE Access, 6:70197–70211, 2018

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This paper cites Bagging and boosting.Encyclopedia of Biostatistics, 1, 2005.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Bagging and boosting.Encyclopedia of Biostatistics, 1, 2005

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This paper cites An overview of ensemble methods for binary classiﬁers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes.Pattern Recognition, 44(8):1761– 1776, 2011.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification An overview of ensemble methods for binary classiﬁers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes.Pattern Recognition, 44(8):1761– 1776, 2011

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This paper cites An empirical study of learning from imbalanced data using random forest.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification An empirical study of learning from imbalanced data using random forest

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Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification A novel ensemble method for imbalanced data learning: bagging of extrapolation-smote svm.Computational intelligence and neuroscience, pages 1–11, 2017

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This paper cites A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches

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Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification A survey of multiple classiﬁer systems as hybrid systems.Information Fusion, 16:3–17, 2014

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This paper cites Comparing boosting and bagging techniques with noisy and imbalanced data.IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 41(3):552–568, 2011.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Comparing boosting and bagging techniques with noisy and imbalanced data.IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 41(3):552–568, 2011

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This paper cites Improving classiﬁers and regions of competence in dynamic ensemble selection.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Improving classiﬁers and regions of competence in dynamic ensemble selection

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This paper cites Philip Kegelmeyer, and Kevin Bowyer.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Philip Kegelmeyer, and Kevin Bowyer

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Observation 58dfe5f6-c810-4973-85cd-72b3a3951302 · outbound

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Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification From dynamic classiﬁer selection to dynamic ensemble selection.Pattern Recognition, 41(5):1718–1731, 2008

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This paper cites Libd3c: ensemble classiﬁers with a clustering and dynamic selection strategy.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Libd3c: ensemble classiﬁers with a clustering and dynamic selection strategy

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Observation 12c5540a-ecbc-48ba-9ca1-88460dc3ac82 · outbound

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Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Meta-des: a dynamic ensemble selection framework using meta-learning.Pattern recognition, 48(5):1925– 1935, 2015

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Observation 314d3d3d-dc39-4986-904b-90c066af644a · outbound

This paper cites Dynamic classiﬁer ensemble model for customer classiﬁcation with imbalanced class distribution.Expert Systems with Applications, 39(3):3668–3675, 2012.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Dynamic classiﬁer ensemble model for customer classiﬁcation with imbalanced class distribution.Expert Systems with Applications, 39(3):3668–3675, 2012

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Observation 290cb5de-dd07-4b3d-9db1-3b213e497bbf · outbound

This paper cites Dynamic ensemble selection for multi-class classiﬁcation with one-class classiﬁers.Pattern Recognition, 83:34–51, 2018.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Dynamic ensemble selection for multi-class classiﬁcation with one-class classiﬁers.Pattern Recognition, 83:34–51, 2018

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Observation 5840a3a6-09e6-44e6-8181-0894cf2797e6 · outbound

This paper cites NoiseOut: A Simple Way to Prune Neural Networks.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification NoiseOut: A Simple Way to Prune Neural Networks

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Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Dropout: a simple way to prevent neural networks from overﬁtting.The Journal of Machine Learning Research, 15(1):1929–1958, 2014

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Observation e9dad6e5-c9da-4228-a026-eae6e7d7d0c5 · outbound

This paper cites Exploratory undersampling for class-imbalance learning.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Exploratory undersampling for class-imbalance learning

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Observation 605c7ee5-585b-4c1d-9d12-e840f9a5a3d8 · outbound

This paper cites An overlap-sensitive margin classiﬁer for imbalanced and overlapping data.Expert Systems with Applications, 98:72–83, 2018.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification An overlap-sensitive margin classiﬁer for imbalanced and overlapping data.Expert Systems with Applications, 98:72–83, 2018

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:41.965287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2d4094f4-0fbe-428f-a07f-c8746bc05ff6 · outbound

This paper cites Iterative regularization for learning with convex loss functions.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Iterative regularization for learning with convex loss functions

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:41.946175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 636b781d-0cc6-4100-b2c5-9b1aad722f73 · outbound

This paper cites Generalization properties and im- plicit regularization for multiple passes sgm.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Generalization properties and im- plicit regularization for multiple passes sgm

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:41.930927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0c879230-23eb-49b6-9dc2-6fca29c148de · outbound

This paper cites Evolutionary under- sampling boosting for imbalanced classiﬁcation of breast cancer malignancy.Applied Soft Computing, 38:714–726, 2016.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Evolutionary under- sampling boosting for imbalanced classiﬁcation of breast cancer malignancy.Applied Soft Computing, 38:714–726, 2016

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:41.916864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4c30905d-cac5-4a74-962f-75e184ba2ef4 · outbound

This paper cites Elblocker: Predicting blocking bugs with ensemble imbalance learning.Information and Software Technology, 61:93– 106, 2015.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Elblocker: Predicting blocking bugs with ensemble imbalance learning.Information and Software Technology, 61:93– 106, 2015

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:41.903152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 74c7a3dc-46e4-41dd-aa31-4fb109a4ae19 · outbound

This paper cites Machine learning based mobile malware detection using highly imbalanced network traﬃc.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Machine learning based mobile malware detection using highly imbalanced network traﬃc

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:41.889675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c728daa5-8033-40e4-944b-f0afd9564189 · outbound

This paper cites A new method for occupational fraud detection in process aware information systems.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification A new method for occupational fraud detection in process aware information systems

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:41.870783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 259f3185-0dfb-420b-b36c-3ffb8b36d937 · outbound

This paper cites On convergence properties of the em algorithm for gaussian mixtures.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification On convergence properties of the em algorithm for gaussian mixtures

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:41.855296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2cbdb434-1a40-4a98-baee-423969e43610 · outbound

This paper cites Scikit-learn: Machine learning in python.Journal of machine learning research, 12(Oct):2825– 2830, 2011.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Scikit-learn: Machine learning in python.Journal of machine learning research, 12(Oct):2825– 2830, 2011

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:41.840947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6012d646-97da-4601-91cc-cdfa0d2fee2a · outbound

This paper cites Sensitivity and speciﬁcity of information criteria.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Sensitivity and speciﬁcity of information criteria

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:41.825141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fbe5a026-7995-430a-aa9d-7662ed2c4de2 · outbound

This paper cites An experiment with the edited nearest-neighbor rule.IEEE Transactions on Systems, Man, and Cybernetics, 1976.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification An experiment with the edited nearest-neighbor rule.IEEE Transactions on Systems, Man, and Cybernetics, 1976

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:41.811302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 940204fe-8126-4eed-a173-6b6caaa100e2 · outbound

This paper cites Friedman.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Friedman

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-14T14:16:41.469966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c329605-e441-4692-a68d-5daf7a619e77 · outbound

This paper cites Keel 3.0: an open source software for multi-stage analysis in data mining.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Keel 3.0: an open source software for multi-stage analysis in data mining

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:41.781867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8f82fb53-3c3e-4114-b93e-1dca44bca13b · outbound

This paper cites Virtual screening of bioassay data.Journal of cheminformatics, 1(1):21, 2009.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Virtual screening of bioassay data.Journal of cheminformatics, 1(1):21, 2009

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:41.763713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0cd3cde1-bdef-4bc2-b8ec-ef61aeee3fe0 · outbound

This paper cites Espíndola and Nelson F.F.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Espíndola and Nelson F.F

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:41.750101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2947a4e8-d6a9-4b90-a334-e0738f06758b · outbound

This paper cites Kernel-based extreme learning machine for remote-sensing image classiﬁcation.Remote Sensing Letters, 4(9):853–862, 2013.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Kernel-based extreme learning machine for remote-sensing image classiﬁcation.Remote Sensing Letters, 4(9):853–862, 2013

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:41.733872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 063cc4d6-47fc-4565-89fa-9b35821359ac · outbound

This paper cites Individual comparisons by ranking methods.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Individual comparisons by ranking methods

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:41.719317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a6795962-8caa-41ad-8bfa-4e4ef04b3bd6 · outbound

This paper cites Approximate statistical tests for comparing supervised classiﬁcation learning algorithms.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Approximate statistical tests for comparing supervised classiﬁcation learning algorithms

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:41.705815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 28824647-ec30-466d-b5ae-4e8f51d7db2a · outbound

This paper cites Nonparametric statistical analysis of machine learning algorithms for regression problems.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Nonparametric statistical analysis of machine learning algorithms for regression problems

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:41.691795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a77434f9-7289-4fb0-9611-d6136bce6dc1 · outbound

This paper cites Statsmodels: Econometric and statistical modeling with python.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Statsmodels: Econometric and statistical modeling with python

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:41.678767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7ccc9786-defb-46f2-af74-675eadff3ed5 · outbound

This paper cites Robust mixture modelling using the t distribution.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Robust mixture modelling using the t distribution

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:41.665684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ca73668a-f1aa-48cf-9667-a4e7db4191b9 · outbound

This paper cites Scalar quantization as sparse least square optimization.IEEE transactions on pattern analysis and machine intelligence, in press, DOI: 10.1109/TPAMI.2019.2952096.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Scalar quantization as sparse least square optimization.IEEE transactions on pattern analysis and machine intelligence, in press, DOI: 10.1109/TPAMI.2019.2952096

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-14T14:16:41.513505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da11ee03-ac4d-4fdc-be4c-c24edd69e7eb · outbound

This paper cites Theeﬀectiveness of lloyd-type methods for the k-means problem.Journal of the ACM (JACM), 59(6):1–22, 2013.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Theeﬀectiveness of lloyd-type methods for the k-means problem.Journal of the ACM (JACM), 59(6):1–22, 2013

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:41.651932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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