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Multi-Label Punitive kNN with Self-Adjusting Memory for Drifting Data Streams

https://doi.org/10.1145/3363573
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Large-Scale Automatic Species Identification
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Learning with Drift Detection
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Random k-Labelsets: An Ensemble Method for Multilabel Classification
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Mining Multi-label Concept-Drifting Data Streams Using Dynamic Classifier Ensemble
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Tackling heterogeneous concept drift with the Self-Adjusting Memory (SAM)
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Ensembles of label noise filters: a ranking approach
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Classifier chains for multi-label classification
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Scalable and efficient multi-label classification for evolving data streams
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An instance level analysis of data complexity
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Multi-label classification via multi-target regression on data streams
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Adaptive random forests for evolving data stream classification
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Discussion and review on evolving data streams and concept drift adapting
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Learning from imbalanced data: open challenges and future directions
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Multi-label classification from high-speed data streams with adaptive model rules and random rules
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Online ensemble learning with abstaining classifiers for drifting and noisy data streams
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Distributed nearest neighbor classification for large-scale multi-label data on spark
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Ensemble learning for data stream analysis: A survey
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LAIM discretization for multi-label data
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Multi-label feature selection with streaming labels
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A survey on data preprocessing for data stream mining: Current status and future directions
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Speeding up k-Nearest Neighbors classifier for large-scale multi-label learning on GPUs
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Online multi-label streaming feature selection based on neighborhood rough set
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Evolving rule-based classifiers with genetic programming on GPUs for drifting data streams
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Semi-supervised learning and social media text analysis towards multi-labeling categorization
inferredcites →https://doi.org/10.1109/bigdata.2018.8622549openalex-snapshot
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Density-Based Core Support Extraction for Non-stationary Environments with Extreme Verification Latency
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inferredcites →https://doi.org/10.1109/iccvw.2009.5457454openalex-snapshot
Online coordinate boosting
inferredcites →https://doi.org/10.1109/icde.2016.7498264openalex-snapshot
Efficient handling of concept drift and concept evolution over Stream Data
inferredcites →https://doi.org/10.1109/icdm.2008.74openalex-snapshot
Multi-label Classification Using Ensembles of Pruned Sets
inferredcites →https://doi.org/10.1109/icdm.2011.63openalex-snapshot
Enabling Fast Lazy Learning for Data Streams
inferredcites →https://doi.org/10.1109/icdm.2016.0040openalex-snapshot
KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift
inferredcites →https://doi.org/10.1109/icdmw.2014.92openalex-snapshot
Drift Detection for Multi-label Data Streams Based on Label Grouping and Entropy
inferredcites →https://doi.org/10.1109/icsmc.2005.1571498openalex-snapshot
Online Bagging and Boosting
inferredcites →https://doi.org/10.1109/ijcnn.2014.6889926openalex-snapshot
Efficient class incremental learning for multi-label classification of evolving data streams
inferredcites →https://doi.org/10.1109/jcai15434.2009openalex-snapshot
2009 International Joint Conference on Artificial Intelligence
inferredcites →https://doi.org/10.1109/lgrs.2018.2868789openalex-snapshot
Multiscale Incremental Dictionary Learning With Label Constraint for SAR Object Recognition
inferredcites →https://doi.org/10.1109/tcyb.2017.2748598openalex-snapshot
Learning From Short Text Streams With Topic Drifts
inferredcites →https://doi.org/10.1109/tevc.2018.2835339openalex-snapshot
IEEE Congress on Evolutionary Computation
inferredcites →https://doi.org/10.1109/tfuzz.2017.2735947openalex-snapshot
Streaming Feature Selection for Multilabel Learning Based on Fuzzy Mutual Information
inferredcites →https://doi.org/10.1109/tip.2018.2839522openalex-snapshot
Object-Location-Aware Hashing for Multi-Label Image Retrieval via Automatic Mask Learning
inferredcites →https://doi.org/10.1109/tkde.2015.2507123openalex-snapshot
Recurring and Novel Class Detection Using Class-Based Ensemble for Evolving Data Stream
inferredcites →https://doi.org/10.1109/tnnls.2012.2236570openalex-snapshot
Active Learning With Drifting Streaming Data
inferredcites →https://doi.org/10.1109/tnnls.2013.2251352openalex-snapshot
Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm
inferredcites →https://doi.org/10.1109/tnnls.2017.2771290openalex-snapshot
A Systematic Study of Online Class Imbalance Learning With Concept Drift
inferredcites →https://doi.org/10.1109/trustcom/bigdatase/icess.2017.328openalex-snapshot
Large-Scale Multi-label Ensemble Learning on Spark
inferredcites →https://doi.org/10.1109/tsmc.2017.2700889openalex-snapshot
Nearest Neighbor Classification for High-Speed Big Data Streams Using Spark
inferredcites →https://doi.org/10.1109/tsp.2018.2880704openalex-snapshot
Computationally Efficient Multi-Agent Multi-Object Tracking With Labeled Random Finite Sets
inferredcites →https://doi.org/10.1137/1.9781611972771.42openalex-snapshot
Learning from Time-Changing Data with Adaptive Windowing
inferredcites →https://doi.org/10.1145/1014052.1014110openalex-snapshot
On demand classification of data streams
inferredcites →https://doi.org/10.1145/2716262openalex-snapshot
A Tutorial on Multilabel Learning
inferredcites →https://doi.org/10.1145/2939672.2939836openalex-snapshot
Fast Unsupervised Online Drift Detection Using Incremental Kolmogorov-Smirnov Test
inferredcites →https://doi.org/10.1145/3106369openalex-snapshot
Data Stream Evolution Diagnosis Using Recursive Wavelet Density Estimators
inferredcites →https://doi.org/10.1145/3139240openalex-snapshot
GOOWE
inferredcites →https://doi.org/10.1145/502512.502529openalex-snapshot
Mining time-changing data streams
inferredcites →https://doi.org/10.1145/956750.956778openalex-snapshot
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inferredcites →https://doi.org/10.24963/ijcai.2019/383openalex-snapshot
Adaptive Ensemble Active Learning for Drifting Data Stream Mining
inferredcites →https://doi.org/10.48550/arxiv.1407.4443openalex-snapshot
On the Complexity of Best Arm Identification in Multi-Armed Bandit Models
inferredcites →https://doi.org/10.5555/1314498.1390333openalex-snapshot
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
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MOA: Massive Online Analysis
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MEKA: A multi-label/multi-target extension to Weka

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Incoming claims: where this work appears in other papers’ reference lists and arguments.

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