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Feature Ranking Derived from Data Mining Process

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F08%3A03145492" target="_blank" >RIV/68407700:21230/08:03145492 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Feature Ranking Derived from Data Mining Process

  • Original language description

    Most common feature ranking methods are based on the statistical approach. This paper compare several statistical methods with new method for feature ranking derived from data mining process. This method ranks features depending on percentage of child units that survived the selection process. A child unit is a processing element transforming the parent input features to the output. After training, units are interconnected in the feedforward hybrid neural network called GAME. The selection process is realized by means of niching genetic algorithm, where units connected to least significant features starve and fade from population. Parameters of new FR algorithm are investigated and comparison among different methods is presented on well known real world and artificial data sets.

  • Czech name

    Feature Ranking Derived from Data Mining Process

  • Czech description

    Most common feature ranking methods are based on the statistical approach. This paper compare several statistical methods with new method for feature ranking derived from data mining process. This method ranks features depending on percentage of child units that survived the selection process. A child unit is a processing element transforming the parent input features to the output. After training, units are interconnected in the feedforward hybrid neural network called GAME. The selection process is realized by means of niching genetic algorithm, where units connected to least significant features starve and fade from population. Parameters of new FR algorithm are investigated and comparison among different methods is presented on well known real world and artificial data sets.

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/KJB201210701" target="_blank" >KJB201210701: Automated Knowledge Extraction</a><br>

  • Continuities

    Z - Vyzkumny zamer (s odkazem do CEZ)

Others

  • Publication year

    2008

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Artificial Neural Networks - ICANN 2008

  • ISBN

    978-3-540-87558-1

  • ISSN

    0302-9743

  • e-ISSN

  • Number of pages

    10

  • Pages from-to

  • Publisher name

    Springer

  • Place of publication

    Heidelberg

  • Event location

    Prague

  • Event date

    Sep 3, 2008

  • Type of event by nationality

    EUR - Evropská akce

  • UT code for WoS article

    000259567200092