Improving feature selection resistance to failures caused by curse-of-dimensionality

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F11%3A00368741" target="_blank" >RIV/67985556:_____/11:00368741 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Improving feature selection resistance to failures caused by curse-of-dimensionality

Popis výsledku v původním jazyce

The purpose of feature selection in machine learning is at least two-fold ? saving measurement acquisition costs and reducing the negative effects of the curse of dimensionality with the aim to improve the accuracy of the models and the classification rate of classifiers with respect to previously unknown data. Yet it has been shown recently that the process of feature selection itself can be negatively affected by the very same curse of dimensionality ? feature selection methods may easily over-fit orperform unstably. Such an outcome is unlikely to generalize well and the resulting recognition system may fail to deliver the expectable performance. In many tasks, it is therefore crucial to employ additional mechanisms of making the feature selection process more stable and resistant the curse of dimensionality effects. In this paper we discuss three different approaches to reducing this problem.

Název v anglickém jazyce

Improving feature selection resistance to failures caused by curse-of-dimensionality

Popis výsledku anglicky

The purpose of feature selection in machine learning is at least two-fold ? saving measurement acquisition costs and reducing the negative effects of the curse of dimensionality with the aim to improve the accuracy of the models and the classification rate of classifiers with respect to previously unknown data. Yet it has been shown recently that the process of feature selection itself can be negatively affected by the very same curse of dimensionality ? feature selection methods may easily over-fit orperform unstably. Such an outcome is unlikely to generalize well and the resulting recognition system may fail to deliver the expectable performance. In many tasks, it is therefore crucial to employ additional mechanisms of making the feature selection process more stable and resistant the curse of dimensionality effects. In this paper we discuss three different approaches to reducing this problem.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název periodika

Kybernetika

ISSN

0023-5954

e-ISSN

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Svazek periodika

47

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

25

Strana od-do

401-425

Kód UT WoS článku

000293207900007

EID výsledku v databázi Scopus

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