Online Active Learning with Drifted Data Streams Using Paired Ensemble Framework
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Summary
A new online paired ensemble active learning framework consisting of a stable classifier and a timely substituted dynamic classifier to react to different types of concept drifts is proposed.
- Type
- article
- Published
- 2017-01-01
- Cited by
- 1
- References
- 22
- Access
- Open access
- OpenAlex
- https://openalex.org/W2752773331
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:51791841
Keywords
Classifier (UML), Computer science, Data stream mining, Machine learning, Concept drift
References
- High density-focused uncertainty sampling for active learning over evolving stream data
- Improving Hoeffding Trees
- Extracting Hidden Context
- Active Learning With Drifting Streaming Data
- Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm
- Mining concept-drifting data streams using ensemble classifiers
- A survey on instance selection for active learning
- Mining high-speed data streams
- Experimental comparisons of online and batch versions of bagging and boosting
- Learning model trees from evolving data streams
- A detailed analysis of the KDD CUP 99 data set
- DDD: A New Ensemble Approach for Dealing with Concept Drift
- MOA: Massive Online Analysis
- Improving Generalization with Active Learning
- Incremental Learning of Concept Drift in Nonstationary Environments
- Active Learning From Stream Data Using Optimal Weight Classifier Ensemble
- Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
- Classifier Ensembles for Detecting Concept Change in Streaming Data: Overview and Perspectives
- Active learning over evolving data streams using paired ensemble framework
- Knowledge discovery from data streams
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