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OFAI-TR-2001-01 ( 55kB g-zipped PostScript file,  117kB PDF file)

An Evaluation of Grading Classifiers

Alexander K. Seewald, Johannes Fürnkranz

In this paper, we introduce grading, a novel meta-classification scheme. While stacking uses the predictions of the base classifiers as meta-level attributes, we use ``graded'' predictions (i.e., predictions that have been marked as correct or incorrect) as meta-level classes. For each base classifier, one meta classifier is learned whose task is to predict when the base classifier will err. Hence, just like stacking may be viewed as a generalization of voting, grading may be viewed as a generalization of selection by cross-validation and therefore fills a conceptual gap in the space of meta-classification schemes. Grading may also be interpreted as a technique for turning the error-characterizing technique introduced by Bay and Pazzani (2000) into a powerful learning algorithm by resorting to an ensemble of meta-classifiers. Our experimental evaluation shows that this step results in a performance gain that is quite comparable to that achieved by stacking, while both, grading and stacking outperform their simpler counter-parts voting and selection by cross-validation.

Keywords: Machine Learning, Classification, Ensembles

Citation: Seewald A., Fürnkranz J.: An Evaluation of Grading Classifiers. In Advances in Intelligent Data Analysis: Proceedings of the 4th International Symposium (IDA-01). Lisbon, Portugal. Springer-Verlag 2001.