Title :
Visual Classification: Expert Knowledge Guides Machine Learning
Author :
MacInnes, J. ; Santosa, S. ; Wright, Wendy
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;
Journal_Title :
Computer Graphics and Applications, IEEE
DOI :
10.1109/MCG.2010.18