Off-Line Learning with Transductive Confidence Machines: An Empirical Evaluation

Authors:
Stijn Vanderlooy;Laurens Maaten;Ida Sprinkhuizen-Kuyper
Affiliations:
MICC-IKAT, Universiteit Maastricht, P.O. Box 616, 6200 MD Maastricht, The Netherlands;MICC-IKAT, Universiteit Maastricht, P.O. Box 616, 6200 MD Maastricht, The Netherlands;NICI, Radboud University Nijmegen, P.O. Box 9104, 6500 HE Nijmegen, The Netherlands
Venue:
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2007

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Abstract

The recently introduced transductive confidence machines (TCMs) framework allows to extend classifiers such that they satisfy the calibration property. This means that the error rate can be set by the user prior to classification. An analytical proof of the calibration property was given for TCMs applied in the on-line learning setting. However, the nature of this learning setting restricts the applicability of TCMs. In this paper we provide strong empirical evidence that the calibration property also holds in the off-line learning setting. Our results extend the range of applications in which TCMs can be applied. We may conclude that TCMs are appropriate in virtually any application domain.