Microsoft Research
Computational User Experiences

Interactive Visualization to Support Machine Learning with Multiple Classifiers

We are working on a new interactive visualization system that presents a graphical view of confusion matrices to help users understand relative merits of various classifiers. It allows users to directly interact with the visualizations in order to explore and build combination models.

 

Project Team

EnsembleMatrix

     

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. We designed and developed a new interactive visualization system that presents a graphical view of confusion matrices to help users under-stand relative merits of various classifiers.

Publications

EnsembleMatrix: Interactive Visualization to Support Machine Learning with Multiple Classifiers

Justin Talbot, Bongshin Lee, Ashish Kapoor, Desney S Tan

Proceedings of ACM CHI 2009, pp. 1283-1292.

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