Visual classification

Joe MacInnes, Stephanie Santosa, William Wright

Research output: Contribution to journalEditorial

4 Citations (Scopus)

Abstract

Humans use intuition and experience to classify everything they perceive, but only if the distinguishing patterns are visible. Machine-learning algorithms can learn class information from data sets, but the created classes' meaning isn't always clear. A proposed mixed-initiative approach combines intuitive visualizations with machine learning to tap into the strengths of human and machine classification. The use of visualizations in an expert-guided clustering technique allows the display of complex data sets in a way that allows human input into machine clustering. Test participants successfully employed this technique to classify analytic activities using behavioral observations of a creative-analysis task. The results demonstrate how visualization of the machine-learned classification can help users create more robust and intuitive categories.

Original languageEnglish
Pages (from-to)8-14
Number of pages7
JournalIEEE Computer Graphics and Applications
Volume30
Issue number1
DOIs
Publication statusPublished - Jan 2010

Cite this

MacInnes, J., Santosa, S., & Wright, W. (2010). Visual classification. IEEE Computer Graphics and Applications, 30(1), 8-14. https://doi.org/10.1109/MCG.2010.18

Visual classification. / MacInnes, Joe; Santosa, Stephanie; Wright, William.

In: IEEE Computer Graphics and Applications, Vol. 30, No. 1, 01.2010, p. 8-14.

Research output: Contribution to journalEditorial

MacInnes, J, Santosa, S & Wright, W 2010, 'Visual classification', IEEE Computer Graphics and Applications, vol. 30, no. 1, pp. 8-14. https://doi.org/10.1109/MCG.2010.18
MacInnes, Joe ; Santosa, Stephanie ; Wright, William. / Visual classification. In: IEEE Computer Graphics and Applications. 2010 ; Vol. 30, No. 1. pp. 8-14.
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