Subject Area
Computer Science
Article Type
Special Issue Original Study
Abstract
Feature selection is a crucial step in machine learning and data preprocessing, significantly influencing model performance and interpretability. This paper presents a comprehensive study and contributions in the domain of feature selection by integrating traditional learning techniques with ensemble-based, proposing an effective approach. We propose a Mutual Information-based feature aggregation approach applied to union sets of features, aiming to derive an optimal subset of features that maximizes accuracy. Then, we employ an ensemble method that utilizes forward selection over union sets to identify the optimal feature subsets through sequential feature selection. Our ensemble-based feature selection method called En-feat, is evaluated using a large number of benchmark datasets. The results have been found highly compared with its other competing methods.
Keywords
Feature Selection; Feature Ensemble; Ensemble Learning; Forward Selection with Backtracking; Random Forest
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Nath, Sasank and Bhattacharyya, Dhruba Kumar
(2026)
"En-feat: An Effective Feature Selection Method Using Ensemble Approach,"
Mansoura Engineering Journal: Vol. 51
:
Iss.
4
, Article 7.
Available at:
https://doi.org/10.58491/2735-4202.3419
