Online Learning From Incomplete and Imbalanced Data Streams-Reference-Cited by-同舟云学术

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Online Learning From Incomplete and Imbalanced Data Streams

Published:2023-10-01 Issue:10 Volume:35 Page:10650-10665
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

You Dianlong¹^ORCID,Xiao Jiawei¹^ORCID,Wang Yang¹^ORCID,Yan Huigui¹,Wu Di²^ORCID,Chen Zhen¹^ORCID,Shen Limin¹^ORCID,Wu Xindong³^ORCID

Affiliation:

1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei, China

2. College of Computer and Information Science, Southwest University, Chongqing, China

3. Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, Hefei, Anhui, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hebei Province

State Scholarship Fund of China Scholarship Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/69/10251471/10058539.pdf?arnumber=10058539

Reference41 articles.

1. Learning With Feature Evolvable Streams

2. Convex Optimization

3. Online Learning from Trapezoidal Data Streams

4. Soft margin multiple kernel learning;xu;IEEE Trans Neural Netw Learn Syst,2013

5. Toward Mining Capricious Data Streams: A Generative Approach

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3. Online Learning for Data Streams With Incomplete Features and Labels;IEEE Transactions on Knowledge and Data Engineering;2024-09

4. Label Shift Correction via Bidirectional Marginal Distribution Matching;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

5. Online learning for data streams with bi-dynamic distributions;Information Sciences;2024-08

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