DIDD: Identifying and Learning New Conceptual Data with Lower Diversity

Chao PAN, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review


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.
Original languageEnglish
Title of host publication2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Print)9781728124858
Publication statusPublished - Dec 2019
Externally publishedYes

Bibliographical note

Thank [16] for providing scikit-multiflow, an open source library for data stream related algorithms. The experimental code of this paper calls the online hoeffding tree algorithm. Thank Dr.Minku for sharing his DDD implementation. This work was supported by the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (Grant No. 2017ZT07X386), Shenzhen Peacock Plan (Grant No. KQTD2016112514355531), and the Program for University Key Laboratory of Guangdong Province (Grant No. 2017KSYS008).


  • concept drift
  • diversity
  • ensemble learning
  • online learning


Dive into the research topics of 'DIDD: Identifying and Learning New Conceptual Data with Lower Diversity'. Together they form a unique fingerprint.

Cite this