| Autor: |
László Göcs, Zsolt Csaba Johanyák |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Computers, Vol 12, Iss 8, p 147 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2073-431X |
| DOI: |
10.3390/computers12080147 |
| Popis: |
Feature selection is a crucial step in machine learning, aiming to identify the most relevant features in high-dimensional data in order to reduce the computational complexity of model development and improve generalization performance. Ensemble feature-ranking methods combine the results of several feature-selection techniques to identify a subset of the most relevant features for a given task. In many cases, they produce a more comprehensive ranking of features than the individual methods used alone. This paper presents a novel approach to ensemble feature ranking, which uses a weighted average of the individual ranking scores calculated using these individual methods. The optimal weights are determined using a Taguchi-type design of experiments. The proposed methodology significantly improves classification performance on the CSE-CIC-IDS2018 dataset, particularly for attack types where traditional average-based feature-ranking score combinations result in low classification metrics. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
