DIDD: Identifying and Learning New Conceptual Data with Lower Diversity

Pan, Chao; Yao, Xin

10.1109/SSCI44817.2019.9002968

2019

978-1-7281-2486-5

2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019

2410-2417

6-9 Dec. 2019

Xiamen, China

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

Institute of Electrical and Electronics Engineers Inc.

The distribution of data streams may change over time, which is called concept drift. Data stream mining algorithms need to detect and adapt to such changes quickly. This paper proposes a new online ensemble algorithm, Diversity and Identification for Dealing with Drifts(DIDD), to tackle the concept drift problem. During the process of concept drift, the data of two concepts exist simultaneously. DIDD uses a snapshot model to find new conceptual data and learn them with lower diversity. Experiments show that DIDD can adapt to new concept more quickly than other online ensemble methods. DIDD has achieved good results on various data sets with different types of concept drift.
© 2019 IEEE.

concept drift online learning ensemble learning diversity

第一

英语

EI ; CPCI-S

[2017ZT07X386]

Computer Science

Computer Science, Artificial Intelligence

WOS:000555467202070

20201108276674

Artificial intelligence

Artificial Intelligence:723.4

EV Compendex

Southern University of Science and Technology, Shenzhen Key Laboratory of Computational Intelligence, University Key Laboratory of Evolving, Intelligent Systems of Guangdong Province, Shenzhen, China

Pan, Chao,Yao, Xin. DIDD: Identifying and Learning New Conceptual Data with Lower Diversity[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:Institute of Electrical and Electronics Engineers Inc.,2019:2410-2417.