@inproceedings{2136,
  abstract     = {{In modern industrial applications driven by Cyber-physical systems (CPS) it is a challenging task to model and optimize processes such as machine analysis and diagnosis. Since the CPS have to act autonomously, a procedure for automated decision making has to be designed. In our work we concentrate on the design of a decision procedure by a fuzzy classifier approach. For our application on decision making in an industrial environment, a fuzzy approach was picked as convenient classification technique regarding balance between accuracy and computational time. We present a supervised learning method called FUZZY-ComRef which combines fuzzy classification and our combinatorial refinement method, called ComRef [1]. Due to the fact that fuzzy classification might behave inaccurately for some datasets, the aim of our approach is to improve the results provided by the (stand-alone) fuzzy classification. We show the performance of FUZZY-ComRef evaluated on the samples from the UCI Repository and on our real-world dataset Motor Drive Diagnosis. In addition, we discuss the quadratic computational time problem arising from the combinatorial nature of ComRef. Furthermore, we show based on real-time evaluations that within parallelisation the proposed FUZZY-ComRef is suitable to many applications in CPS.}},
  author       = {{Dörksen, Helene and Lohweg, Volker}},
  booktitle    = {{20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Luxembourg, Sep 2015. }},
  keywords     = {{Support vector machines, Accuracy, Time complexity, Decision making, Motor drives, Shape, Sensors}},
  publisher    = {{IEEE}},
  title        = {{{Automated Fuzzy Classification with Combinatorial Refinement}}},
  doi          = {{ 10.1109/ETFA.2015.7301514}},
  year         = {{2015}},
}