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Incremental Learning of Concept Drift from Streaming Imbalanced Data
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
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Classifier Ensembles for Changing Environments
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SMOTEBoost: Improving Prediction of the Minority Class in Boosting
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An Ensemble Approach for Incremental Learning in Nonstationary Environments
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Incremental Learning of Variable Rate Concept Drift
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Incremental Learning of New Classes in Unbalanced Datasets: Learn + + .UDNC
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Incremental Learning from Noisy Data
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Learning in the Presence of Concept Drift and Hidden Contexts
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Tracking Drifting Concepts By Minimizing Disagreements
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Towards incremental learning of nonstationary imbalanced data stream: a multiple selectively recursive approach
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Info-fuzzy algorithms for mining dynamic data streams
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Mining decision rules on data streams in the presence of concept drifts
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Real-time data mining of non-stationary data streams from sensor networks
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Dynamic integration of classifiers for handling concept drift
inferredcites →https://doi.org/10.1016/s0304-3975(00)00274-7openalex-snapshot
Predictive learning models for concept drift
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Life-long learning Cell Structures—continuously learning without catastrophic interference
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Machine Learning for the Detection of Oil Spills in Satellite Radar Images
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Learn++: an incremental learning algorithm for supervised neural networks
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Dynamic weighted majority: a new ensemble method for tracking concept drift
inferredcites →https://doi.org/10.1109/icdm.2006.66openalex-snapshot
Entropy-based Concept Shift Detection
inferredcites →https://doi.org/10.1109/icdm.2007.96openalex-snapshot
On Appropriate Assumptions to Mine Data Streams: Analysis and Practice
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Heuristic Updatable Weighted Random Subspaces for Non-stationary Environments
inferredcites →https://doi.org/10.1109/iciafs.2007.4544780openalex-snapshot
Use of Hoeffding trees in concept based data stream mining
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Adaptive Classifiers-Ensemble System for Tracking Concept Drift
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An Incremental Learning Algorithm for Non-stationary Environments and Class Imbalance
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Formal models of incremental learning and their analysis
inferredcites →https://doi.org/10.1109/ijcnn.2008.4634290openalex-snapshot
Learning concept drift in nonstationary environments using an ensemble of classifiers based approach
inferredcites →https://doi.org/10.1109/ijcnn.2009.5178779openalex-snapshot
Incremental learning in nonstationary environments with controlled forgetting
inferredcites →https://doi.org/10.1109/ijcnn.2009.5178799openalex-snapshot
Just in time classifiers: Managing the slow drift case
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SERA: Selectively recursive approach towards nonstationary imbalanced stream data mining
inferredcites →https://doi.org/10.1109/ijcnn.2010.5596537openalex-snapshot
Change detection tests using the ICI rule
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MuSeRA: Multiple Selectively Recursive Approach towards imbalanced stream data mining
inferredcites →https://doi.org/10.1109/ijcnn.2010.5596764openalex-snapshot
An ensemble based incremental learning framework for concept drift and class imbalance
inferredcites →https://doi.org/10.1109/mic.2008.119openalex-snapshot
Classifying Data Streams with Skewed Class Distributions and Concept Drifts
inferredcites →https://doi.org/10.1109/tfuzz.2010.2042721openalex-snapshot
FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning
inferredcites →https://doi.org/10.1109/tit.1968.1054155openalex-snapshot
The condensed nearest neighbor rule (Corresp.)
inferredcites →https://doi.org/10.1109/tkde.2010.36openalex-snapshot
Classification Using Streaming Random Forests
inferredcites →https://doi.org/10.1109/tnn.2008.2000082openalex-snapshot
Just-in-Time Adaptive Classifiers—Part I: Detecting Nonstationary Changes
inferredcites →https://doi.org/10.1109/tnn.2008.2001774openalex-snapshot
IMORL: Incremental Multiple-Object Recognition and Localization
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Just-in-Time Adaptive Classifiers—Part II: Designing the Classifier
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Learn$^{++}$.NC: Combining Ensemble of Classifiers With Dynamically Weighted Consult-and-Vote for Efficient Incremental Learning of New Classes
inferredcites →https://doi.org/10.1109/tnn.2011.2160459openalex-snapshot
Incremental Learning of Concept Drift in Nonstationary Environments
inferredcites →https://doi.org/10.1109/tsmcb.2008.2002909openalex-snapshot
SVMs Modeling for Highly Imbalanced Classification
inferredcites →https://doi.org/10.1137/1.9781611972771.1openalex-snapshot
A General Framework for Mining Concept-Drifting Data Streams with Skewed Distributions
inferredcites →https://doi.org/10.1145/1007730.1007736openalex-snapshot
Learning from imbalanced data sets with boosting and data generation
inferredcites →https://doi.org/10.1145/1233341.1233378openalex-snapshot
Classifying imbalanced data using a bagging ensemble variation (BEV)
inferredcites →https://doi.org/10.1145/1557019.1557041openalex-snapshot
New ensemble methods for evolving data streams
inferredcites →https://doi.org/10.1145/1656274.1656287openalex-snapshot
Adaptive learning and mining for data streams and frequent patterns
inferredcites →https://doi.org/10.1145/502512.502529openalex-snapshot
Mining time-changing data streams
inferredcites →https://doi.org/10.1145/502512.502568openalex-snapshot
A streaming ensemble algorithm (SEA) for large-scale classification
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SMOTE: Synthetic Minority Over-sampling Technique
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Statistical Comparisons of Classifiers over Multiple Data Sets
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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No Free Lunch Theorem for concept drift detection in streaming data classification: A review
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Majority Voting Algorithm for Diagnosing of Imbalanced Malaria Disease
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Machine Learning Approaches for IC Manufacturing Yield Enhancement
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The General Procedure of Ensembles Construction in Data Stream Scenarios
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Multi Sampling Random Subspace Ensemble for Imbalanced Data Stream Classification
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LPG-model: A novel model for throughput prediction in stream processing, using a light gradient boosting machine, incremental principal component analysis, and deep gated recurrent unit network
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An adaptive algorithm for dealing with data stream evolution and singularity
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Synthetic Sample Generation for Label Distribution Learning
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LUNAR: Cellular automata for drifting data streams
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Automatic online multi-source domain adaptation
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Active Weighted Aging Ensemble for drifted data stream classification
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Graph-based method for autonomous adaptation in online learning of non-stationary data
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Adaptive stochastic configuration network based on online active learning for evolving data streams
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TRACED: Trend-adaptive classification with ellipsoidal disambiguation for resolving exterior and coincident regions in data streams
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Semi-supervised adaptive learning for streaming data in internet of things applications
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Concept drift detection and adaptation with hierarchical hypothesis testing
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Hellinger Distance Weighted Ensemble for imbalanced data stream classification
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Incremental entropy-based clustering on categorical data streams with concept drift
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High utility drift detection in quantitative data streams
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Entropy and gravitation based dynamic radius nearest neighbor classification for imbalanced problem
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Incremental learning imbalanced data streams with concept drift: The dynamic updated ensemble algorithm
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Significance of activation functions in developing an online classifier for semiconductor defect detection
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Pro-IDD: Pareto-based ensemble for imbalanced and drifting data streams
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Cost-sensitive continuous ensemble kernel learning for imbalanced data streams with concept drift
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Hybrid Firefly Optimised Ensemble Classification for Drifting Data Streams with Imbalance
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Incremental learning fault detection for UAVs in non-stationary wind environments
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A-iLearn: An adaptive incremental learning model for spoof fingerprint detection
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Ensemble of subset online sequential extreme learning machine for class imbalance and concept drift
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Scaffolding type-2 classifier for incremental learning under concept drifts
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Data driven modelling based on Recurrent Interval-Valued Metacognitive Scaffolding Fuzzy Neural Network
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SOM-based partial labeling of imbalanced data stream
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Kernel based online learning for imbalance multiclass classification
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Incremental on-line learning: A review and comparison of state of the art algorithms
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A taxonomic look at instance-based stream classifiers
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The Gradual Resampling Ensemble for mining imbalanced data streams with concept drift
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OTL-CE : Online transfer learning for data streams with class evolution
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Meta-cognitive online sequential extreme learning machine for imbalanced and concept-drifting data classification
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MixGradient: A gradient-based re-weighting scheme with mixup for imbalanced data streams
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Adaptive skew-sensitive ensembles for face recognition in video surveillance
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Persistence in complex systems
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Classifier Ensemble Design for Imbalanced Data Classification: A Hybrid Approach
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The 10 most popular Concept Drift Algorithms: An overview and optimization potentials
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Scalable Detection of Concept Drift: A Learning Technique Based on Support Vector Machines
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Proactive computer network monitoring based on homogeneous deep neural ensemble
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On botnet detection with genetic programming under streaming data label budgets and class imbalance
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Maritime vessel traffic modeling in the context of concept drift
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Incremental learning algorithm for dynamic evolution of domain specific vocabulary with its stability and plasticity analysis
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Fast online dynamic voltage instability prediction and voltage stability classification
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Dynamic classification approach using scalable ensemble of autoencoders to classify data with drift
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Battering Review Spam Through Ensemble Learning in Imbalanced Datasets
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Enhanced Data Stream Classification by Optimized Weight Updated Meta-learning: Continuous learning-based on Concept-Drift
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Deep Data Stream Analysis Model and Algorithm With Memory Mechanism
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A Multi-Tier Stacked Ensemble Algorithm to Reduce the Regret of Incremental Learning for Streaming Data
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An Overview on Concept Drift Learning
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Variance Ranking Attributes Selection Techniques for Binary Classification Problem in Imbalance Data
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Resample-Based Ensemble Framework for Drifting Imbalanced Data Streams
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Expected Quantization Error Stability-Based Self-Organizing Growth Neural Network With Adaptive Output Network Scale
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Two-Stage Cost-Sensitive Learning for Data Streams With Concept Drift and Class Imbalance
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Dynamic Modeling With Integrated Concept Drift Detection for Predicting Real-Time Energy Consumption of Industrial Machines
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Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches
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A Broad Ensemble Learning System for Drifting Stream Classification
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A Systematic Literature Review of Concept Drift Mitigation in Time-Series Applications
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OS-λ1-ELM: Online sequential λ1-regularized-ELM based on ADMM
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Improved model for dynamic ensemble selection based on hybrid sampling
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A distributed framework for supporting adaptive ensemble-based intrusion detection
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Adaptive Hoeffding Tree with Transfer Learning for Streaming Synchrophasor Data Sets
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Active Learning with Abstaining Classifiers for Imbalanced Drifting Data Streams
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Concept Drift and Covariate Shift Detection Ensemble with Lagged Labels
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Tenet: Benchmarking Data Stream Classifiers in Presence of Temporal Dependence
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IGMM-CD: A Gaussian Mixture Classification Algorithm for Data Streams with Concept Drifts
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Incremental Weighted and Adaptive Sliding Window Based Classification Algorithm for Imbalanced Data Stream
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A New Hybrid Sampling Approach for Classification of Imbalanced Datasets
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Adapting to concept drift with genetic programming for classifying streaming data
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Incremental learning and novelty detection of gestures using extreme value theory
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A learning framework for online class imbalance learning
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Intrusion Detection based on Incremental Learning
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Incremental Author Name Disambiguation for Scientific Citation Data
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The Value of Diversity for Dealing with Concept Drift in Class-Imbalanced Data Streams
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Fraud Analysis Approaches in the Age of Big Data - A Review of State of the Art
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A Concept for Proactive Knowledge Construction in Self-Learning Autonomous Systems
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An incremental interval Type-2 neural fuzzy Classifier
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Online Popularity Prediction of Video Segments: Towards More Efficient Content Delivery Networks
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A Peer-to-Peer Architecture for Detecting Attacks from Network Traffic and Log Data
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DynaGraph-FL: Dynamic Federated Graph Learning with Privacy, Robustness, and Drift Handling
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Exploring effect of preprocessing on classifier ensembles in imbalanced dataset classification
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Online and incremental machine learning approaches for IC yield improvement
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