期刊论文、会议论文

Wen, Yimin

英文

Proceedings of the International Joint Conference on Neural Networks

2161-4393

2014

2093-2099

International Joint Conference on Neural Networks (IJCNN)

IEEE International Joint Conference on Neural Networks (IJCNN)

JUL 06-11, 2014

Beijing, PEOPLES R CHINA

[Shi, Zhongwei] Guilin Univ Elect Technol, Sch Comp Sci & Engn, Guilin, Peoples R China.^[Wen, Yimin] Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin, Peoples R China.^[Xue, Yun] Hunan City Univ, Sch Municipal & Surveying Engn, Changsha, Hunan, Peoples R China.^[Cai, Guoyong] Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin, Peoples R China.

IEEE

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

978-1-4799-1484-5

10.1109/IJCNN.2014.6889926

本校为其他机构

Multi-label stream classification has not been fully explored for the unique properties of large data volumes, realtime, label dependencies, etc. Some methods try to take into account label dependencies, but they only focus on the existing frequent label combinations, leading to worse performance for multi-label classification. To deal with these problems, this paper proposes an algorithm which dynamically recognizes some new frequent label combinations and updates the trained classifier by class incremental learning strategy. Experimental results over both real-world and synthetic datasets de...