DocumentCode :
1372253
Title :
Visual Classification: Expert Knowledge Guides Machine Learning
Author :
MacInnes, J. ; Santosa, S. ; Wright, Wendy
Volume :
30
Issue :
1
fYear :
2010
Firstpage :
8
Lastpage :
14
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.
Keywords :
data visualisation; expert systems; learning (artificial intelligence); pattern classification; pattern clustering; creative-analysis task; expert knowledge; expert-guided clustering technique; machine clustering; machine learning; visual classification; Clustering algorithms; Data visualization; Displays; Humans; Machine learning; Machine learning algorithms; Robustness; Testing; classification; computer graphics; graphics and multimedia.; machine learning; mixed-initiative interfaces; visualization; workflow modeling;
fLanguage :
English
Journal_Title :
Computer Graphics and Applications, IEEE
Publisher :
ieee
ISSN :
0272-1716
Type :
jour
DOI :
10.1109/MCG.2010.18
Filename :
5370737
Link To Document :
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