---
res:
  bibo_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.@eng'
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Helene
      foaf_name: Dörksen, Helene
      foaf_surname: Dörksen
      foaf_workInfoHomepage: http://www.librecat.org/personId=46416
  - foaf_Person:
      foaf_givenName: Volker
      foaf_name: Lohweg, Volker
      foaf_surname: Lohweg
      foaf_workInfoHomepage: http://www.librecat.org/personId=1804
    orcid: 0000-0002-3325-7887
  bibo_doi: 10.5220/0006115502930300
  dct_date: 2017^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/9789897582226
  dct_language: eng
  dct_publisher: SCITEPRESS - Science and Technology Publications, Lda.@
  dct_subject:
  - Refinement of Classification
  - Robust Classification
  - Classification within Small/Incomplete Samples
  dct_title: Margin-based Refinement for Support-Vector-Machine Classification@
...