---
res:
  bibo_abstract:
  - We present a method for the fast and robust linear classification of badly conditioned
    data. In our considerations, badly conditioned data are such data which are numerically
    difficult to handle. Due to, e.g. a large number of features or a large number
    of objects representing classes as well as noise, outliers or incompleteness,
    the common software computation of the discriminating linear combination of features
    between classes fails or is extremely time consuming. The theoretical foundations
    of our approach are based on the single feature ranking, which allows fast calculation
    of the approximative initial classification boundary. For the increasing of classification
    accuracy of this boundary, the refinement is performed in the lower dimensional
    space. Our approach is tested on several datasets from UCI Reposi-tiory. Experimental
    results indicate high classification accuracy of the approach. For the modern
    real industrial applications such a method is especially suitable in the Cyber-Physical-System
    environments and provides a part of the workflow for the automated classifier
    design@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: 10.1109/ETFA.2018.8502485
  dct_date: 2018^xs_gYear
  dct_language: eng
  dct_subject:
  - Task analysis
  - Software
  - Linear discriminant analysis
  - Dimensionality reduction
  - Mathematical model
  - Covariance matrices
  - Measurement
  dct_title: Linear Classification of Badly Conditioned Data. @
...