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@INPROCEEDINGS{moewes_tackling_2008,
     author = {Moewes, Christian and Otte, Clemens and Kruse, Rudolf},
     editor = {Dubois, Didier and Lubiano, M. Asunci{\'{o}}n and Prade, Henri and Gil, Mar{\'{\i}}a {\'{A}}ngeles and Grzegorzewski, Przemys{\l}aw and Hryniewicz, Olgierd},
   keywords = {Multiple-Instance Learning, Safety-Related Systems, Support Vector Machine},
      month = oct,
      title = {Tackling Multiple-Instance Problems in Safety-Related Domains by Quasilinear {SVM}},
  booktitle = {Soft Methods for Handling Variability and Imprecision},
     series = {Advances in Soft Computing},
     volume = {48},
       year = {2008},
      pages = {409--416},
  publisher = {Springer Berlin/Heidelberg},
       issn = {1615-3871},
       isbn = {978-3-540-85026-7},
        url = {http://springerlink.com/content/870h7h003w457740/},
        doi = {10.1007/978-3-540-85027-4_49},
   abstract = {In this paper we introduce a preprocessing method for safety-related applications. Since we concentrate on scenarios with highly unbalanced misclassification costs, we briefly discuss a variation of multiple-instance learning (MIL) and recall soft margin hyperplane classifiers; in particular the principle of a support vector machine (SVM). According to this classifier, we present a training set selection method for learning quasilinear SVMs which guarantee both high accuracy and interpretability to a higher degree. We conclude with annotating on a real-world application and potential extensions for future research in this domain.}
}