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
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DOI - Digital Object Identifier
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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
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BD - Information theory
OECD FORD branch
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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
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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
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UT code for WoS article
000281990900001
EID of the result in the Scopus database
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