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2009
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EnsembleMatrix: interactive visualization to support machine learning with multiple classifiers

11 years 2 months ago
EnsembleMatrix: interactive visualization to support machine learning with multiple classifiers
Machine learning is an increasingly used computational tool within human-computer interaction research. While most researchers currently utilize an iterative approach to refining classifier models and performance, we propose that ensemble classification techniques may be a viable and even preferable alternative. In ensemble learning, algorithms combine multiple classifiers to build one that is superior to its components. In this paper, we present EnsembleMatrix, an interactive visualization system that presents a graphical view of confusion matrices to help users understand relative merits of various classifiers. EnsembleMatrix allows users to directly interact with the visualizations in order to explore and build combination models. We evaluate the efficacy of the system and the approach in a user study. Results show that users are able to quickly combine multiple classifiers operating on multiple feature sets to produce an ensemble classifier with accuracy that approaches best-repor...
Justin Talbot, Bongshin Lee, Ashish Kapoor, Desney
Added 24 Nov 2009
Updated 24 Nov 2009
Type Conference
Year 2009
Where CHI
Authors Justin Talbot, Bongshin Lee, Ashish Kapoor, Desney S. Tan
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