- Tytuł:
- A weighted wrapper approach to feature selection
- Autorzy:
-
Kusy, Maciej
Zajdel, Roman - Powiązania:
- https://bibliotekanauki.pl/articles/2055180.pdf
- Data publikacji:
- 2021
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
feature selection
wrapper approach
feature significance
weighted combined ranking
convolutional neural network
classification accuracy
selekcja cech
sieć neuronowa konwolucyjna
dokładność klasyfikacji - Opis:
- 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.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2021, 31, 4; 685--696
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł