What you see is what you can change: Human-centered machine learning by interactive visualization

Dominik Sacha*, Michael Sedlmair, Leishi Zhang, John A. Lee, Jaakko Peltonen, Daniel Weiskopf, Stephen C. North, Daniel A. Keim

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

37 Citations (Scopus)

Abstract

Visual analytics (VA) systems help data analysts solve complex problems interactively, by integrating automated data analysis and mining, such as machine learning (ML) based methods, with interactive visualizations. We propose a conceptual framework that models human interactions with ML components in the VA process, and that puts the central relationship between automated algorithms and interactive visualizations into sharp focus. The framework is illustrated with several examples and we further elaborate on the interactive ML process by identifying key scenarios where ML methods are combined with human feedback through interactive visualization. We derive five open research challenges at the intersection of ML and visualization research, whose solution should lead to more effective data analysis. (C) 2017 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)164-175
Number of pages12
JournalNeurocomputing
Volume268
DOIs
Publication statusPublished - 13 Dec 2017
MoE publication typeA1 Journal article-refereed

Keywords

  • Machine learning
  • Information visualization
  • Interaction
  • Visual analytics
  • VISUAL ANALYTICS
  • INFORMATION VISUALIZATION
  • DIMENSIONALITY REDUCTION
  • UNCERTAINTY
  • PERSPECTIVE
  • DIRECTIONS
  • FRAMEWORK
  • SELECTION
  • MODELS

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