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An analysis on new hybrid parameter selection model performance over big data set

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F20%3A50017065" target="_blank" >RIV/62690094:18450/20:50017065 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.sciencedirect.com/science/article/pii/S0950705119306628" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0950705119306628</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.knosys.2019.105441" target="_blank" >10.1016/j.knosys.2019.105441</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    An analysis on new hybrid parameter selection model performance over big data set

  • Original language description

    Parameter selection or attribute selection is one of the crucial tasks in the data analysis process. Incorrect selection of the important attribute might generate imprecise or event for a wrong decision. It is an advantage if the decision-maker could select and apply the best model that helps in identifying the best-optimized attribute set — in the decision analysis process. Recently, many data scientists from various application areas are attracted to investigate and analyze the advantages and disadvantages of big data. One of the issues is, analyzing large volumes and variety of data in a big data environment is very challenging to the data scientists when there is a lack of a suitable model or no appropriate model to be implemented and used as a guideline. Hence, this paper proposes an alternative parameterization model that is able to generate the most optimized attribute set without requiring a high cost to learn, to use, and to maintain. The model is based on two integrated models that are combined with correlation-based feature selection, best-first search algorithm, soft set, and rough set theories which were compliments to each other as a parameter selection method. Experimental have shown that the proposed model has significantly shown as an alternative model in a big data analysis process. © 2020 The Authors

  • 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

    2020

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Knowledge-based systems

  • ISSN

    0950-7051

  • e-ISSN

  • Volume of the periodical

    192

  • Issue of the periodical within the volume

    March

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    11

  • Pages from-to

    "Article Number: 105441"

  • UT code for WoS article

    000519335400041

  • EID of the result in the Scopus database

    2-s2.0-85078493275