A Projection-Based Framework for Classifier Performance Evaluation

Authors:
Nathalie Japkowicz;Pritika Sanghi;Peter Tischer
Affiliations:
School of Information Technology and Engineering, University of Ottawa, Canada;Clayton School of Information technology, Monash University, Melbourne, Australia;Clayton School of Information technology, Monash University, Melbourne, Australia
Venue:
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Year:
2008

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Abstract

In this paper, we propose approaching the problem of classifier evaluation in terms of a projection from a high-dimensional space to a visualizable two-dimensional one. 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 performance 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. Furthermore, by providing simultaneous multiple views of the same evaluation data, it allows for a quick and accurate assessment of classifier performance.