Correlation-based Feature Ranking in Combination with Embedded Feature Selection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F09%3A00159293" target="_blank" >RIV/68407700:21230/09:00159293 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21240/09:00159293

Výsledek na webu

<a href="http://iwim2009.felk.cvut.cz/files/ProceedingsIWIM09.pdf" target="_blank" >http://iwim2009.felk.cvut.cz/files/ProceedingsIWIM09.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Correlation-based Feature Ranking in Combination with Embedded Feature Selection

Popis výsledku v původním jazyce

Most of Feature Ranking and Feature Selection approaches can be used for categorial data only. In this paper we present new methods for feature ranking and selection obtained as a combination of the above mentioned approaches. The data mining algorithm (GAME) is designed for numerical data, but it can be applied to categorial data as well. It incorporates feature selection mechanisms and new methods, proposed in this paper, derive feature ranking from final data mining model. The rank of each feature selected by model is computed by processing correlations of outputs between neighboring model's neurons in different ways. We used four different methods based on fuzzy logic, certainty factors and simple calculus. The performance of these four feature ranking methods was tested on artificial data sets and on well known real world data sets. These methods produce ranking consistent with recently published studies.

Název v anglickém jazyce

Correlation-based Feature Ranking in Combination with Embedded Feature Selection

Popis výsledku anglicky

Most of Feature Ranking and Feature Selection approaches can be used for categorial data only. In this paper we present new methods for feature ranking and selection obtained as a combination of the above mentioned approaches. The data mining algorithm (GAME) is designed for numerical data, but it can be applied to categorial data as well. It incorporates feature selection mechanisms and new methods, proposed in this paper, derive feature ranking from final data mining model. The rank of each feature selected by model is computed by processing correlations of outputs between neighboring model's neurons in different ways. We used four different methods based on fuzzy logic, certainty factors and simple calculus. The performance of these four feature ranking methods was tested on artificial data sets and on well known real world data sets. These methods produce ranking consistent with recently published studies.

Druh

O - Ostatní výsledky

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/KJB201210701" target="_blank" >KJB201210701: Automatická extrakce znalostí</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2009

Kód důvěrnosti údajů

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