Abstract
Relying on an ordinal relationship among class labels, ordinal classifiers incorporate semantic knowledge about the classes into a purely data-driven multi-class classification task. Under the assumption that this relationship is reflected in feature space, these classifiers organize their internals according to this information. One essential step required is the identification of the true inter-class dependencies.
In this work, we now focus on the ability of cascaded ensemble classifiers to detect the relationships among ordinal classes. The minimal class sensitivity proves to be suitable to quantify this ability. This is an important problem, as for instance in medical applications often the true ordering of the classes is unknown or only partly known. We show that we can detect the ordinal class structure or its absence and that this ability depends on both the chosen base classifiers and the corresponding training schemes.
R. Lattke and L. Lausser—Contributed equally.
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Acknowledgements
The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/20072013) under grant agreement n\(^\circ \)602783 (to HAK), the German Research Foundation (DFG, SFB 1074 project Z1 to HAK), and the Federal Ministry of Education and Research (BMBF, Gerontosys II, Forschungskern SyStaR, project ID 0315894A to HAK).
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Lattke, R., Lausser, L., Müssel, C., Kestler, H.A. (2015). Detecting Ordinal Class Structures. In: Schwenker, F., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2015. Lecture Notes in Computer Science(), vol 9132. Springer, Cham. https://doi.org/10.1007/978-3-319-20248-8_9
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DOI: https://doi.org/10.1007/978-3-319-20248-8_9
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