Object

Title: A weighted wrapper approach to feature selection

Creator:

Kusy, Maciej ; Zajdel, Roman

Date:

2021

Resource Type:

artykuł

Contributor:

Kusy, Maciej - ed. ; Scherer, Rafał - ed. ; Krzyżak, Adam - ed.

Subtitle:

.

Group publication title:

AMCS, volume 31 (2021)

Abstract:

This paper considers feature selection as a problem of an aggregation of three state-of-the-art filtration methods: Pearson`s linear correlation coefficient, the ReliefF algorithm and decision trees. A new wrapper method is proposed which, on the basis of a fusion of the above approaches and the performance of a classifier, is capable of creating a distinct, ordered subset of attributes that is optimal based on the criterion of the highest classification accuracy obtainable by a convolutional neural network. The introduced feature selection uses a weighted ranking criterion. ; In order to evaluate the effectiveness of the solution, the idea is compared with sequential feature selection methods that are widely known and used wrapper approaches. Additionally, to emphasize the need for dimensionality reduction, the results obtained on all attributes are shown. The verification of the outcomes is presented in the classification tasks of repository data sets that are characterized by a high dimensionality. The presented conclusions confirm that it is worth seeking new solutions that are able to provide a better classification result while reducing the number of input features.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Resource Identifier:

oai:zbc.uz.zgora.pl:86351

DOI:

10.34768/amcs-2021-0047

Pages:

685-696

Source:

AMCS, volume 31, number 4 (2021) ; click here to follow the link

Language:

eng

License CC BY 4.0:

click here to follow the link

Rights:

Biblioteka Uniwersytetu Zielonogórskiego

Edition name Date
A weighted wrapper approach to feature selection Jul 22, 2025

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