<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

A weighted wrapper approach to feature selection

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"A weighted wrapper approach to feature selection"}

This paper considers feature selection as a problem of an aggregation of three state-of-the-art filtration methods: Pearson’s linear correlation coefficient,...

A weighted wrapper approach to feature selection

Online veröffentlicht: 30. Dez. 2021

Seitenbereich: 685 - 696

Eingereicht: 24. Feb. 2021

Akzeptiert: 04. Okt. 2021

DOI: https://doi.org/10.34768/amcs-2021-0047

Schlüsselwörter
feature selection, wrapper approach, feature significance, weighted combined ranking, convolutional neural network, classification accuracy

© 2021 Maciej Kusy et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A weighted wrapper approach to feature selection

Online veröffentlicht: 30. Dez. 2021

Seitenbereich: 685 - 696

Eingereicht: 24. Feb. 2021

Akzeptiert: 04. Okt. 2021

DOI: https://doi.org/10.34768/amcs-2021-0047

Schlüsselwörterfeature selection, wrapper approach, feature significance, weighted combined ranking, convolutional neural network, classification accuracy

© 2021 Maciej Kusy et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
feature selection, wrapper approach, feature significance, weighted combined ranking, convolutional neural network, classification accuracy