Func-Bagging: An Ensemble Learning Strategy for Improving the Performance of Heterogeneous Anomaly Detection Models
In the field of ensemble learning, bagging and stacking are two widely used ensemble strategies. Bagging enhances model robustness through repeated sampling and weighted averaging of homogeneous classifiers, while stacking improves classification performance by integrating multiple models using meta...
Saved in:
Main Authors: | Ruinan Qiu, Yongfeng Yin, Qingran Su, Tianyi Guan |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-01-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/15/2/905 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
An approach of Bagging ensemble based on feature set and application for traffic classification
by: Yaguan QIAN, et al.
Published: (2018-04-01) -
Enhancing breast cancer prediction through stacking ensemble and deep learning integration
by: Fatih Gurcan
Published: (2025-02-01) -
High-Performance stacking ensemble learning for thermoelectric figure-of-merit prediction
by: Yuelin Wang, et al.
Published: (2025-01-01) -
Paper Bags vis-à-vis LDPE Bags: Gleanings from Peer-reviewed E-LCA Publications
by: Isabell Lidbrand, et al.
Published: (2023-08-01) -
Detecting Anomaly Classification Using PCA-Kmeans and Ensembled Classifier for Wind Turbines
by: Prince Waqas Khan, et al.
Published: (2024-01-01)