Classifier Utility Visualization by Distance-Preserving Projection of High Dimensional Performance Data

Nathalie Japkowicz, Pritika Sanghi, Peter Tischer

In this paper, we propose to view the problem of classifier evaluation in terms of a projection from a high-dimensional space to a visualizable two-dimensional space. Rather than collapsing confusion matrices into a single measure the way traditional evaluation methods do, we consider the vector composed of the entries of the confusion matrix (or the confusion matrices in case several domains are considered simultaneously) as the evaluation vector and project it into a two dimensional space using a recently proposed distance-preserving projection method. This approach is shown to be particularly useful in the case of comparison of several classifiers on many domains as well as in the case of multiclass classification.

Subjects: 12. Machine Learning and Discovery; 9. Foundational Issues

Submitted: May 12, 2007


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