Unsupervised Concept Drift Detection Using Dynamic Crucial Feature Distribution Test in Data Streams

Yen Ning Wan, Prasad Jaysawal Bijay, Jen Wei Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Distribution of data often changes over time and leads to the unpredictable changes in the implicit information behind data streams. This phenomenon is referred to as Concept Drift. The accuracy of conventional models reduces as time goes by, and old models are rendered impractical. In this paper, we propose a novel approach for solving the concept drift detection problem using the unsupervised method and focusing on the dynamic crucial feature distribution test. Extensive experiments have been done to evaluate the performance of the proposed method against classic and state-of-the-art methods. Experimental results demonstrate the efficacy of the proposed model when applied to synthetic as well as real-world datasets.

Original languageEnglish
Title of host publicationProceedings - 2022 International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages137-142
Number of pages6
ISBN (Electronic)9798350399509
DOIs
Publication statusPublished - 2022
Event27th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2022 - Tainan, Taiwan
Duration: 2022 Dec 12022 Dec 3

Publication series

NameProceedings - 2022 International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2022

Conference

Conference27th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2022
Country/TerritoryTaiwan
CityTainan
Period22-12-0122-12-03

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Hardware and Architecture
  • Control and Optimization

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