Abstract
Classifier performance evaluation typically gives rise to a multitude of results that are difficult to interpret. On the one hand, a variety of different performance metrics can be applied, each adding a little bit more information about the classifiers than the others; and on the other hand, evaluation must be conducted on multiple domains to get a clear view of the classifier’s general behaviour.
Supported by the Natural Science and Engineering Council of Canada and the Spanish MEC projects TEC2005-06766-C03-01 and DPI2006-02550.
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Alaiz-Rodríguez, R., Japkowicz, N., Tischer, P. (2008). A Visualization-Based Exploratory Technique for Classifier Comparison with Respect to Multiple Metrics and Multiple Domains. In: Daelemans, W., Goethals, B., Morik, K. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2008. Lecture Notes in Computer Science(), vol 5212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87481-2_43
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DOI: https://doi.org/10.1007/978-3-540-87481-2_43
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