The Problem of Fragile Feature Subset Preference in Feature Selection Methods and A Proposal of Algorithmic Workaround

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F10%3A00348710" target="_blank" >RIV/67985556:_____/10:00348710 - isvavai.cz</a>
Alternative codes found
RIV/61384399:31160/10:00036189
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
The Problem of Fragile Feature Subset Preference in Feature Selection Methods and A Proposal of Algorithmic Workaround
Original language description
We point out a problem inherent in the optimization scheme of many popular feature selection methods. It follows from the implicit assumption that higher feature selection criterion value always indicates more preferable subset even if the value difference is marginal. This assumption ignores the reliability issues of particular feature preferences, overfitting and feature acquisition cost. We propose an algorithmic extension applicable to many standard feature selection methods allowing better controlover feature subset preference. We show experimentally that the proposed mechanism is capable of reducing the size of selected subsets as well as improving classifier generalization.
Czech name
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Czech description
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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)

Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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