Tackling Multiple-Instance Problems in Safety-Related Domains by Quasilinear {SVM}
| Type of publication: | Inproceedings |
| Citation: | moewes_tackling_2008 |
| Booktitle: | Soft Methods for Handling Variability and Imprecision |
| Series: | Advances in Soft Computing |
| Volume: | 48 |
| Year: | 2008 |
| Month: | October |
| Pages: | 409--416 |
| Publisher: | Springer Berlin/Heidelberg |
| ISSN: | 1615-3871 |
| ISBN: | 978-3-540-85026-7 |
| URL: | http://springerlink.com/conten... |
| 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. |
| Keywords: | Multiple-Instance Learning, Safety-Related Systems, Support Vector Machine |
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| Added by: | [ADM] |
| Total mark: | 0 |
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