Abstract
The aggregation of multiple unstable classifiers leads to substantial reduction of misclassification error in many applications and bench mark problems. We focus on the problem of classifying eyes as normal or glaucomatous based on measurements derived from laser scanning images of the optic nerve head. The performance of various aggregated classifiers is investigated for a clinical training sample and for a simulation model of eye morphologies.
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© 2002 Springer-Verlag Berlin Heidelberg
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Hothorn, T., Lausen, B. (2002). Bagging Tree Classifiers for Glaucoma Diagnosis. In: Härdle, W., Rönz, B. (eds) Compstat. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-57489-4_23
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DOI: https://doi.org/10.1007/978-3-642-57489-4_23
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1517-7
Online ISBN: 978-3-642-57489-4
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