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SYNTHETIC DATA GENERATOR FOR TESTING OF CLASSIFICATION RULE ALGORITHMS

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F17%3A43914967" target="_blank" >RIV/60461373:22340/17:43914967 - isvavai.cz</a>

  • Result on the web

    <a href="http://www.nnw.cz/doi/2017/NNW.2017.27.010.pdf" target="_blank" >http://www.nnw.cz/doi/2017/NNW.2017.27.010.pdf</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.14311/NNW.2017.27.010" target="_blank" >10.14311/NNW.2017.27.010</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    SYNTHETIC DATA GENERATOR FOR TESTING OF CLASSIFICATION RULE ALGORITHMS

  • Original language description

    We developed a data generating system that is able to create systematically testing datasets that accomplish user&apos;s requirements such as number of rows, number and type of attributes, number of missing values, class noise and imbalance ratio. These datasets can be used for testing of the algorithms designed for solving classification rule problem. We used them for optimizing of the parameters of the classification algorithm based on the behavior of ant colonies. But they can be advantageously used for other applications too. Program generates output files in ARFF format. Two standards and one user-define probability distributions are used in data generation: uniform distribution, normal distribution and irregular distribution for nominal attributes. To our knowledge, our system is probably the first synthetic data generation system that systematically generates datasets for examination and judgment of the classification rule algorithms.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2017

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Neural Network World

  • ISSN

    1210-0552

  • e-ISSN

  • Volume of the periodical

    27

  • Issue of the periodical within the volume

    2

  • Country of publishing house

    CZ - CZECH REPUBLIC

  • Number of pages

    15

  • Pages from-to

    215-229

  • UT code for WoS article

    000402020800003

  • EID of the result in the Scopus database