Training Set Fuzzification Towards Prediction Improvement

  • Eva VolnaEmail author
  • Jaroslav Zacek
  • Robert Jarusek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10334)


This article presents a method of fuzzification of variables using a histogram. This approach is used when creating an output vector of a training set that forms linguistic variables. An appropriate transformation of an input vector of the training sets was also proposed. Both of the aforesaid procedures were described in detail in the article. An extensive comparative experimental study with the following outcomes was carried out. The neural net which was adapted by the transformed training set showed a significantly better prediction than a neural network which was adapted by a training set without making any changes. The results of this experimental study were analyzed in the conclusion.


Continuous distribution Histogram Linguistic variable Neural network Prediction 



The research described here has been financially supported by University of Ostrava grant SGS/PRF/2017.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Informatics and ComputersUniversity of OstravaOstravaCzech Republic

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