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https://doi.org/10.2139/ssrn.4682920

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inferredcites →https://doi.org/10.1007/11538059_91openalex-extracted
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning
inferredcites →https://doi.org/10.1007/3-540-33019-4_19openalex-extracted
Trade-Off Between Diversity and Accuracy in Ensemble Generation
inferredcites →https://doi.org/10.1007/978-3-540-75488-6_27openalex-extracted
Detecting Concept Drift Using Statistical Testing
inferredcites →https://doi.org/10.1007/978-3-642-21222-2_19openalex-extracted
Accuracy Updated Ensemble for Data Streams with Concept Drift
inferredcites →https://doi.org/10.1007/s00521-020-05386-5openalex-extracted
Constructing accuracy and diversity ensemble using Pareto-based multi-objective learning for evolving data streams
inferredcites →https://doi.org/10.1007/s10115-021-01560-wopenalex-extracted
The impact of data difficulty factors on classification of imbalanced and concept drifting data streams
inferredcites →https://doi.org/10.1007/s10994-017-5642-8openalex-extracted
Adaptive random forests for evolving data stream classification
inferredcites →https://doi.org/10.1007/s10994-022-06168-xopenalex-extracted
ROSE: robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams
inferredcites →https://doi.org/10.1007/s12530-013-9076-7openalex-extracted
Recursive least square perceptron model for non-stationary and imbalanced data stream classification
inferredcites →https://doi.org/10.1016/j.compchemeng.2014.03.015openalex-extracted
Cognitive fault diagnosis in Tennessee Eastman Process using learning in the model space
inferredcites →https://doi.org/10.1016/j.eswa.2018.03.021openalex-extracted
Adapting dynamic classifier selection for concept drift
inferredcites →https://doi.org/10.1016/j.ins.2013.07.007openalex-extracted
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
inferredcites →https://doi.org/10.1016/j.ins.2017.11.046openalex-extracted
Knowledge-maximized ensemble algorithm for different types of concept drift
inferredcites →https://doi.org/10.1016/j.ins.2018.06.056openalex-extracted
Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE
inferredcites →https://doi.org/10.1016/j.ins.2023.02.046openalex-extracted
Active Weighted Aging Ensemble for drifted data stream classification
inferredcites →https://doi.org/10.1016/j.knosys.2020.106631openalex-extracted
On the class overlap problem in imbalanced data classification
inferredcites →https://doi.org/10.1109/access.2020.3031603openalex-extracted
Two-Stage Cost-Sensitive Learning for Data Streams With Concept Drift and Class Imbalance
inferredcites →https://doi.org/10.1109/bigdata50022.2020.9377768openalex-extracted
C-SMOTE: Continuous Synthetic Minority Oversampling for Evolving Data Streams
inferredcites →https://doi.org/10.1109/bracis.2015.61openalex-extracted
IGMM-CD: A Gaussian Mixture Classification Algorithm for Data Streams with Concept Drifts
inferredcites →https://doi.org/10.1109/cec.2015.7256954openalex-extracted
KernelADASYN: Kernel based adaptive synthetic data generation for imbalanced learning
inferredcites →https://doi.org/10.1109/ciel.2013.6613138openalex-extracted
A learning framework for online class imbalance learning
inferredcites →https://doi.org/10.1109/icdmw.2006.18openalex-extracted
A Probabilistic Ensemble Pruning Algorithm
inferredcites →https://doi.org/10.1109/icdmw51313.2020.00121openalex-extracted
Incremental Rebalancing Learning on Evolving Data Streams
inferredcites →https://doi.org/10.1109/tcyb.2021.3053165openalex-extracted
When Does Diversity Help Generalization in Classification Ensembles?
inferredcites →https://doi.org/10.1109/tkde.2009.62openalex-extracted
Predictive Ensemble Pruning by Expectation Propagation
inferredcites →https://doi.org/10.1109/tkde.2010.26openalex-extracted
Multiobjective Neural Network Ensembles Based on Regularized Negative Correlation Learning
inferredcites →https://doi.org/10.1109/tkde.2012.232openalex-extracted
MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning
inferredcites →https://doi.org/10.1109/tkde.2013.157openalex-extracted
Cost-Sensitive Online Classification
inferredcites →https://doi.org/10.1109/tkde.2014.2345380openalex-extracted
Resampling-Based Ensemble Methods for Online Class Imbalance Learning
inferredcites →https://doi.org/10.1109/tkde.2016.2609424openalex-extracted
Online Bagging and Boosting for Imbalanced Data Streams
inferredcites →https://doi.org/10.1109/tkde.2022.3179381openalex-extracted
An Investigation of SMOTE based Methods for Imbalanced Datasets with Data Complexity Analysis
inferredcites →https://doi.org/10.1109/tnn.2008.2000082openalex-extracted
Just-in-Time Adaptive Classifiers—Part I: Detecting Nonstationary Changes
inferredcites →https://doi.org/10.1109/tnn.2011.2160459openalex-extracted
Incremental Learning of Concept Drift in Nonstationary Environments
inferredcites →https://doi.org/10.1109/tnnls.2017.2771290openalex-extracted
A Systematic Study of Online Class Imbalance Learning With Concept Drift
inferredcites →https://doi.org/10.1109/tnnls.2022.3183120openalex-extracted
Dynamic Ensemble Selection for Imbalanced Data Streams With Concept Drift
inferredcites →https://doi.org/10.1111/coin.12520openalex-extracted
A survey of active and passive concept drift handling methods
inferredcites →https://doi.org/10.1111/exsy.12661openalex-extracted
Two‐level pruning based ensemble with abstained learners for concept drift in data streams
inferredcites →https://doi.org/10.1145/1007730.1007735openalex-extracted
A study of the behavior of several methods for balancing machine learning training data
inferredcites →https://doi.org/10.1145/1281192.1281264openalex-extracted
Statistical change detection for multi-dimensional data
inferredcites →https://doi.org/10.1145/2487575.2487700openalex-extracted
Model-based kernel for efficient time series analysis
inferredcites →https://doi.org/10.1145/3341105.3373949openalex-extracted
Cost-sensitive learning for imbalanced data streams
inferredcites →https://doi.org/10.1145/3570960openalex-extracted
A Weighted Ensemble Classification Algorithm Based on Nearest Neighbors for Multi-Label Data Stream
inferredcites →https://doi.org/10.1145/502512.502568openalex-extracted
A streaming ensemble algorithm (SEA) for large-scale classification
inferredcites →https://doi.org/10.1145/956750.956778openalex-extracted
Mining concept-drifting data streams using ensemble classifiers
inferredcites →https://doi.org/10.1613/jair.953openalex-extracted
SMOTE: Synthetic Minority Over-sampling Technique
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