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A survey on ensemble learning for data stream classification
Ensemble-based methods are among the most widely used techniques for data stream
classification. Their popularity is attributable to their good performance in comparison to …
classification. Their popularity is attributable to their good performance in comparison to …
[کتاب][B] Machine learning for data streams: with practical examples in MOA
A Bifet, R Gavalda, G Holmes, B Pfahringer - 2023 - books.google.com
A hands-on approach to tasks and techniques in data stream mining and real-time analytics,
with examples in MOA, a popular freely available open-source software framework. Today …
with examples in MOA, a popular freely available open-source software framework. Today …
A survey on feature drift adaptation: Definition, benchmark, challenges and future directions
Data stream mining is a fast growing research topic due to the ubiquity of data in several real-
world problems. Given their ephemeral nature, data stream sources are expected to …
world problems. Given their ephemeral nature, data stream sources are expected to …
Data stream mining techniques: a review
A plethora of infinite data is generated from the Internet and other information sources.
Analyzing this massive data in real-time and extracting valuable knowledge using different …
Analyzing this massive data in real-time and extracting valuable knowledge using different …
Map** the big data landscape: technologies, platforms and paradigms for real-time analytics of data streams
TheBig Data'of yesterday is thedata'of today. As technology progresses, new challenges
arise and new solutions are developed. Due to the emergence of Internet of Things …
arise and new solutions are developed. Due to the emergence of Internet of Things …
Real-time feature selection technique with concept drift detection using adaptive micro-clusters for data stream mining
Data streams are unbounded, sequential data instances that are generated with high
Velocity. Classifying sequential data instances is a very challenging problem in machine …
Velocity. Classifying sequential data instances is a very challenging problem in machine …
Towards efficient and scalable acceleration of online decision tree learning on FPGA
Decision trees are machine learning models commonly used in various application
scenarios. In the era of big data, traditional decision tree induction algorithms are not …
scenarios. In the era of big data, traditional decision tree induction algorithms are not …
A survey on feature drift adaptation
Mining data streams is of the utmost importance due to its appearance in many real-world
situations, such as: sensor networks, stock market analysis and computer networks intrusion …
situations, such as: sensor networks, stock market analysis and computer networks intrusion …
Analyzing the impact of feature drifts in streaming learning
Learning from data streams requires efficient algorithms capable of deriving a model
accordingly to the arrival of new instances. Data streams are by definition unbounded …
accordingly to the arrival of new instances. Data streams are by definition unbounded …
Hard-odt: Hardware-friendly online decision tree learning algorithm and system
Decision trees are machine learning models commonly used in various application
scenarios. In the era of big data, traditional decision tree induction algorithms are not …
scenarios. In the era of big data, traditional decision tree induction algorithms are not …