Online reliable semi-supervised learning on evolving data streams
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Summary
A new online semi-supervised learning algorithm is proposed by modeling concept drifts with a set of micro-clusters that are dynamically maintained to capture the evolving concepts with error-based representative learning and yield high classification performance compared to many state-of-the-art algorithms.
- Type
- article
- Published
- 2020-07-01
- Cited by
- 92
- References
- 49
- OpenAlex
- https://openalex.org/W3013371665
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:216519685
Keywords
Computer science, Data stream mining, Concept drift, Streaming data, Data stream
References
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- Self-training from labeled features for sentiment analysis
- IBLStreams: a system for instance-based classification and regression on data streams
- COMPOSE: A Semisupervised Learning Framework for Initially Labeled Nonstationary Streaming Data
- Combining labeled and unlabeled data with co-training
- Mining high-speed data streams
- Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples
- Deep learning in partially-labeled data streams
- A survey on concept drift adaptation
- A Practical Approach to Classify Evolving Data Streams: Training with Limited Amount of Labeled Data
- Combining block-based and online methods in learning ensembles from concept drifting data streams
- Semi-Supervised Classification on Evolutionary Data
Cited by
- An Online Semantic-enhanced Dirichlet Model for Short Text Stream Clustering
- Data stream clustering: a review
- Incremental Learning from Low-labelled Stream Data in Open-Set Video Face Recognition
- A novel semi-supervised ensemble algorithm using a performance-based selection metric to non-stationary data streams
- Recurring Drift Detection and Model Selection-Based Ensemble Classification for Data Streams with Unlabeled Data
- Deep semi‐supervised classification based in deep clustering and cross‐entropy
- Learning High-Dimensional Evolving Data Streams With Limited Labels
- CLASSIFICATION BASED ON SEMI-SUPERVISED LEARNING: A REVIEW
- LaPOLeaF: Label propagation in an optimal leading forest
- A Survey on Semi-supervised Learning for Delayed Partially Labelled Data Streams
- Data stream classification with novel class detection: a review, comparison and challenges
- Cost-effective and adaptive clustering algorithm for stream processing on cloud system
- A Clustering-based Framework for Classifying Data Streams
- Learning in the Presence of Skew and Missing Labels Through Online Ensembles and Meta-reinforcement Learning
- Online Clustering Technique with Adaptable Threshold and Radius for Evolving Data Stream
- An Online Semantic-Enhanced Graphical Model for Evolving Short Text Stream Clustering
- Handling concept drifts and limited label problems using semi-supervised combine-merge Gaussian mixture model
- CPSSDS: Conformal prediction for semi-supervised classification on data streams
- Toward feasible machine learning model updates in network-based intrusion detection
- A dynamic hierarchical incremental learning-based supervised clustering for data stream with considering concept drift
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