Abstract
The drawback of many state-of-the-art classifiers is that their models are not easily interpretable. We recently introduced Representative Prototype Sets (RPS), which are simple base classifiers that allow for a systematic description of data sets by exhaustive enumeration of all possible classifiers.
The major focus of the previous work was on a descriptive characterization of low-cardinality data sets. In the context of prediction, a lack of accuracy of the simple RPS model can be compensated by accumulating the decisions of several classifiers. Here, we now investigate ensembles of RPS base classifiers in a predictive setting on data sets of high dimensionality and low cardinality. The performance of several selection and fusion strategies is evaluated. We visualize the decisions of the ensembles in an exemplary scenario and illustrate links between visual data set inspection and prediction.
Christoph Müssel and Ludwig Lausser have contributed equally to this work.
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Acknowledgements
This work is supported by the German Science Foundation (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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Müssel, C., Lausser, L., Kestler, H.A. (2015). Ensembles of Representative Prototype Sets for Classification and Data Set Analysis. In: Lausen, B., Krolak-Schwerdt, S., Böhmer, M. (eds) Data Science, Learning by Latent Structures, and Knowledge Discovery. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44983-7_29
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