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Training set fuzzification towards prediction improvement

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F17%3AA1801QXX" target="_blank" >RIV/61988987:17310/17:A1801QXX - isvavai.cz</a>

  • Result on the web

    <a href="https://link.springer.com/chapter/10.1007%2F978-3-319-59650-1_18" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-319-59650-1_18</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-319-59650-1_18" target="_blank" >10.1007/978-3-319-59650-1_18</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Training set fuzzification towards prediction improvement

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10200 - Computer and information sciences

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2017

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Lecture Notes in Artificial Intelligence

  • ISBN

    978-331959649-5

  • ISSN

    0302-9743

  • e-ISSN

  • Number of pages

    13

  • Pages from-to

    207-219

  • Publisher name

    Springer Verlag

  • Place of publication

    Cham, Switzerland

  • Event location

    La Rioja; Spain

  • Event date

    Jun 21, 2017

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article