TY  - CONF
ID  - moewes_tackling_2008
T1  - Tackling Multiple-Instance Problems in Safety-Related Domains by Quasilinear {SVM}
A1  - Moewes, Christian
A1  - Otte, Clemens
A1  - Kruse, Rudolf
ED  - Dubois, Didier
ED  - Lubiano, M. Asunción
ED  - Prade, Henri
ED  - Gil, María Ángeles
ED  - Grzegorzewski, Przemysław
ED  - Hryniewicz, Olgierd
TI  - Soft Methods for Handling Variability and Imprecision
T3  - Advances in Soft Computing
Y1  - 2008
VL  - 48
SP  - 409
EP  - 416
PB  - Springer Berlin/Heidelberg
SN  - 978-3-540-85026-7
SN  - 1615-3871
UR  - http://springerlink.com/content/870h7h003w457740/
M2  - doi: 10.1007/978-3-540-85027-4_49
KW  - Multiple-Instance Learning
KW  - Safety-Related Systems
KW  - Support Vector Machine
N2  - 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.
ER  -