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Evaluating Stability and Comparing Output of Feature Selectors that Optimize Feature Subset Cardinality

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F10%3A00348726" target="_blank" >RIV/67985556:_____/10:00348726 - isvavai.cz</a>

  • Alternative codes found

    RIV/61384399:31160/10:00036187

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Evaluating Stability and Comparing Output of Feature Selectors that Optimize Feature Subset Cardinality

  • Original language description

    Stability (robustness) of feature selection methods is a topic of recent interest, yet often neglected importance, with direct impact on the reliability of machine learning systems. We investigate the problem of evaluating the stability of feature selection processes yielding subsets of varying size. We introduce several novel feature selection stability measures and adjust some existing measures in a unifying framework that offers broad insight into the stability problem. We study in detail the properties of considered measures and demonstrate on various examples what information about the feature selection process can be gained. We also introduce an alternative approach to feature selection evaluation in the form of measures that enable comparing thesimilarity of two feature selection processes. These measures enable comparing, e.g., the output of two feature selection methods or two runs of one method with different parameters.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

  • CEP classification

    BD - Information theory

  • OECD FORD branch

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    Z - Vyzkumny zamer (s odkazem do CEZ)

Others

  • Publication year

    2010

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    IEEE Transactions on Pattern Analysis and Machine Intelligence

  • ISSN

    0162-8828

  • e-ISSN

  • Volume of the periodical

    32

  • Issue of the periodical within the volume

    11

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    19

  • Pages from-to

  • UT code for WoS article

    000281990900001

  • EID of the result in the Scopus database