Obiekt

Tytuł: A weighted wrapper approach to feature selection

Autor:

Kusy, Maciej ; Zajdel, Roman

Data wydania:

2021

Typ zasobu:

artykuł

Współtwórca:

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

Podtytuł:

.

Tytuł publikacji grupowej:

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.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Identyfikator zasobu:

oai:zbc.uz.zgora.pl:86351

DOI:

10.34768/amcs-2021-0047

Strony:

685-696

Źródło:

AMCS, volume 31, number 4 (2021) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Licencja CC BY 4.0:

kliknij tutaj, żeby przejść

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego

Nazwa wydania Data
A weighted wrapper approach to feature selection 22 lip 2025

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