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

Abstract

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.

Original languageEnglish
Title of host publication2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2410-2417
Number of pages8
ISBN (Print)9781728124858
DOIs
Publication statusPublished - Dec 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Funding

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).

Keywords

  • concept drift
  • diversity
  • ensemble learning
  • online learning

Fingerprint

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

Cite this