@inproceedings{2015,
  abstract     = {{ In real-world scenarios it is not always possible to generate an appropriate number of measured objects for machine learning tasks. At the learning stage, for small/incomplete datasets it is nonetheless often possible to get high accuracies for several arbitrarily chosen classifiers. The fact is that many classifiers might perform accurately, but decision boundaries might be inadequate. In this situation, the decision supported by marginlike characteristics for the discrimination of classes might be taken into account. Accuracy as an exclusive measure is often not sufficient. To contribute to the solution of this problem, we present a margin-based approach originated from an existing refinement procedure. In our method, margin value is considered as optimisation criterion for the refinement of SVM models. The performance of the approach is evaluated on a real-world application dataset for Motor Drive Diagnosis coming from the field of intelligent autonomous systems in the context of I ndustry 4.0 paradigm as well as on several UCI Repository samples with different numbers of features and objects.}},
  author       = {{Dörksen, Helene and Lohweg, Volker}},
  booktitle    = {{Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods}},
  isbn         = {{9789897582226}},
  keywords     = {{Refinement of Classification, Robust Classification, Classification within Small/Incomplete Samples}},
  location     = {{Porto, Protugal}},
  pages        = {{293--300}},
  publisher    = {{SCITEPRESS - Science and Technology Publications, Lda.}},
  title        = {{{Margin-based Refinement for Support-Vector-Machine Classification}}},
  doi          = {{10.5220/0006115502930300}},
  year         = {{2017}},
}