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Feature Selection with Fuzzy Entropy to Find Similar Cases

József Mezei, Juan Antonio Morente-Molinera, Christer Carlsson, Feature Selection with Fuzzy Entropy to Find Similar Cases. In: Mo Jamshidi, Vladik Kreinovich, Janusz Kacprzyk (Eds.), Advance Trends in Soft Computing, Studies in Fuzziness and Soft Computing 312, 383–390, Springer, 2013.


Process interruptions are carried out either automatically by monitoring and control systems that react to deviations from standards or by operators reacting to anomalies or incidents. Process interruptions in (very) large production systems are difficult to trace and to deal with; an extended stop is also very costly and solutions are sought to find an effective support technology to minimize the number of involuntary process interruptions. Feature selection is intended to reduce the complexity of handling the interactions of numerous factors in large process systems and to help find the best ways to handle process interruptions. We show that feature selection can be carried out with fuzzy entropy and interval-valued fuzzy sets.

BibTeX entry:

  title = {Feature Selection with Fuzzy Entropy to Find Similar Cases},
  booktitle = {Advance Trends in Soft Computing},
  author = {Mezei, József and Morente-Molinera, Juan Antonio and Carlsson, Christer},
  volume = {312},
  series = {Studies in Fuzziness and Soft Computing},
  editor = {Jamshidi, Mo and Kreinovich, Vladik and Kacprzyk, Janusz},
  publisher = {Springer},
  pages = {383–390},
  year = {2013},

Belongs to TUCS Research Unit(s): Institute for Advanced Management Systems Research (IAMSR)

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