Extremely Fast Decision Tree Mining for Evolving Data Streams

Author:

Bifet Albert1,Zhang Jiajin2,Fan Wei3,He Cheng2,Zhang Jianfeng2,Qian Jianfeng4,Holmes Geoff5,Pfahringer Bernhard5

Affiliation:

1. Telecom ParisTech, Paris, France

2. HUAWEI, Hong Kong, Hong Kong

3. Baidu Research Big Data Lab, Sunnyvale, CA, USA

4. Columbia University, New York, NY, USA

5. University of Waikato, Hamilton, New Zealand

Publisher

ACM

Cited by 52 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Bin.INI: An ensemble approach for dynamic data streams;Expert Systems with Applications;2024-12

2. An Intelligent Edge Dual-Structure Ensemble Method for Data Stream Detection and Releasing;IEEE Internet of Things Journal;2024-01-01

3. Pro-IDD: Pareto-based ensemble for imbalanced and drifting data streams;Knowledge-Based Systems;2023-12

4. Mondrian forest for data stream classification under memory constraints;Data Mining and Knowledge Discovery;2023-10-17

5. StreamMLOps: Operationalizing Online Learning for Big Data Streaming & Real-Time Applications;2023 IEEE 39th International Conference on Data Engineering (ICDE);2023-04

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