Abstract
We propose three transductive versions of the set covering machine with data dependent rays for classification in the molecular high-throughput setting. Utilizing both labeled and unlabeled samples, these transductive classifiers can learn information from both sample types, not only from labeled ones. These transductive set covering machines are based on modified selection criteria for their ensemble members. Via counting arguments we include the unlabeled information into the base classifier selection. One of the three methods we developed, uniformly increased the classification accuracy, the other two showed mixed behaviour for all data sets. Here, we could show that only by observing the order of unlabeled samples, not distances, we were able to increase classification accuracies, making these approaches useful even when very few information is available.
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Notes
- 1.
We only utilize the information of orderings of features values, not the information given by a ordinal structure of the class labels (e.g. Herbrich et al. 1999).
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Acknowledgements
This work was funded in part by a Karl-Steinbuch grant to Florian Schmid, the German federal ministry of education and research (BMBF) within the framework of the program of medical genome research (PaCa-Net; Project ID PKB-01GS08) and the framework GERONTOSYS 2 (Forschungskern SyStaR, Project ID 0315894A), and by the German Science Foundation (SFB 1074, Project Z1) to Hans A. Kestler. The responsibility for the content lies exclusively with the authors.
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Schmid, F., Lausser, L., Kestler, H.A. (2014). Three Transductive Set Covering Machines. In: Spiliopoulou, M., Schmidt-Thieme, L., Janning, R. (eds) Data Analysis, Machine Learning and Knowledge Discovery. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-01595-8_33
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DOI: https://doi.org/10.1007/978-3-319-01595-8_33
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