Göcs, L.; Johanyák, Z.C. Feature Selection with Weighted Ensemble Ranking for Improved Classification Performance on the CSE-CIC-IDS2018 Dataset. Computers2023, 12, 147.
Göcs, L.; Johanyák, Z.C. Feature Selection with Weighted Ensemble Ranking for Improved Classification Performance on the CSE-CIC-IDS2018 Dataset. Computers 2023, 12, 147.
Göcs, L.; Johanyák, Z.C. Feature Selection with Weighted Ensemble Ranking for Improved Classification Performance on the CSE-CIC-IDS2018 Dataset. Computers2023, 12, 147.
Göcs, L.; Johanyák, Z.C. Feature Selection with Weighted Ensemble Ranking for Improved Classification Performance on the CSE-CIC-IDS2018 Dataset. Computers 2023, 12, 147.
Abstract
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 its 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 in them. This paper presents a novel approach to ensemble feature ranking, which uses a weighted average of the individual ranking scores calculated by the 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 resulted in low classification metrics.
Computer Science and Mathematics, Computer Networks and Communications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
