- Home
- Publications
- Publication Search
- Publication Details
Title
A Segment-Based Drift Adaptation Method for Data Streams
Authors
Keywords
-
Journal
IEEE Transactions on Neural Networks and Learning Systems
Volume 33, Issue 9, Pages 4876-4889
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Online
2021-04-10
DOI
10.1109/tnnls.2021.3062062
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Fuzzy Clustering-Based Adaptive Regression for Drifting Data Streams
- (2020) Yiliao Song et al. IEEE TRANSACTIONS ON FUZZY SYSTEMS
- Analyzing concept drift and shift from sample data
- (2018) Geoffrey I. Webb et al. DATA MINING AND KNOWLEDGE DISCOVERY
- Accumulating regional density dissimilarity for concept drift detection in data streams
- (2018) Anjin Liu et al. PATTERN RECOGNITION
- A pdf-Free Change Detection Test Based on Density Difference Estimation
- (2018) Li Bu et al. IEEE Transactions on Neural Networks and Learning Systems
- A Systematic Study of Online Class Imbalance Learning With Concept Drift
- (2018) Shuo Wang et al. IEEE Transactions on Neural Networks and Learning Systems
- Concept Drift Adaptation by Exploiting Historical Knowledge
- (2018) Yu Sun et al. IEEE Transactions on Neural Networks and Learning Systems
- New Splitting Criteria for Decision Trees in Stationary Data Streams
- (2018) Maciej Jaworski et al. IEEE Transactions on Neural Networks and Learning Systems
- An Incremental Learning of Concept Drifts Using Evolving Type-2 Recurrent Fuzzy Neural Networks
- (2017) Mahardhika Pratama et al. IEEE TRANSACTIONS ON FUZZY SYSTEMS
- Ensemble learning for data stream analysis: A survey
- (2017) Bartosz Krawczyk et al. Information Fusion
- Adaptive random forests for evolving data stream classification
- (2017) Heitor M. Gomes et al. MACHINE LEARNING
- Hierarchical Change-Detection Tests
- (2017) Cesare Alippi et al. IEEE Transactions on Neural Networks and Learning Systems
- A concept drift-tolerant case-base editing technique
- (2016) Ning Lu et al. ARTIFICIAL INTELLIGENCE
- Recognizing input space and target concept drifts in data streams with scarcely labeled and unlabelled instances
- (2016) Edwin Lughofer et al. INFORMATION SCIENCES
- Adaptive Model Rules From High-Speed Data Streams
- (2016) João Duarte et al. ACM Transactions on Knowledge Discovery from Data
- Learning in Nonstationary Environments: A Survey
- (2015) Gregory Ditzler et al. IEEE Computational Intelligence Magazine
- A New Method for Data Stream Mining Based on the Misclassification Error
- (2015) Leszek Rutkowski et al. IEEE Transactions on Neural Networks and Learning Systems
- A survey on concept drift adaptation
- (2014) João Gama et al. ACM COMPUTING SURVEYS
- Active Learning With Drifting Streaming Data
- (2013) Indre Zliobaite et al. IEEE Transactions on Neural Networks and Learning Systems
- Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm
- (2013) Dariusz Brzezinski et al. IEEE Transactions on Neural Networks and Learning Systems
- DDD: A New Ensemble Approach for Dealing with Concept Drift
- (2011) Leandro L. Minku et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- Incremental Learning of Concept Drift in Nonstationary Environments
- (2011) R. Elwell et al. IEEE TRANSACTIONS ON NEURAL NETWORKS
- Learning model trees from evolving data streams
- (2010) Elena Ikonomovska et al. DATA MINING AND KNOWLEDGE DISCOVERY
- Model Averaging via Penalized Regression for Tracking Concept Drift
- (2010) Kyupil Yeon et al. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreDiscover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversation