Diagnosing Concept Drift with Visual Analytics

Weikai Yang, Zhen Li, Mengchen Liu, Yafeng Lu, Kelei Cao, Ross MacIejewski, Shixia Liu

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

1 Scopus citations

Abstract

Concept drift is a phenomenon in which the distribution of a data stream changes over time in unforeseen ways, causing prediction models built on historical data to become inaccurate. While a variety of automated methods have been developed to identify when concept drift occurs, there is limited support for analysts who need to understand and correct their models when drift is detected. In this paper, we present a visual analytics method, DriftVis, to support model builders and analysts in the identification and correction of concept drift in streaming data. DriftVis combines a distribution-based drift detection method with a streaming scatterplot to support the analysis of drift caused by the distribution changes of data streams and to explore the impact of these changes on the model's accuracy. A quantitative experiment and two case studies on weather prediction and text classification have been conducted to demonstrate our proposed tool and illustrate how visual analytics can be used to support the detection, examination, and correction of concept drift.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE Conference on Visual Analytics Science and Technology, VAST 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages12-23
Number of pages12
ISBN (Electronic)9781728180090
DOIs
StatePublished - Oct 2020
Event15th IEEE Conference on Visual Analytics Science and Technology, VAST 2020 - Virtual, Salt Lake City, United States
Duration: Oct 25 2020Oct 30 2020

Publication series

NameProceedings - 2020 IEEE Conference on Visual Analytics Science and Technology, VAST 2020

Conference

Conference15th IEEE Conference on Visual Analytics Science and Technology, VAST 2020
CountryUnited States
CityVirtual, Salt Lake City
Period10/25/2010/30/20

Keywords

  • Concept drift
  • change detection
  • scatterplot
  • streaming data
  • t-SNE.

ASJC Scopus subject areas

  • Media Technology
  • Modeling and Simulation

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