A Comparative Analysis of Combination of CNN-Based Models with Ensemble Learning on Imbalanced Data
This study investigates the usefulness of the Synthetic Minority Oversampling Technique (SMOTE) in conjunction with convolutional neural network (CNN) models, which include both single and ensemble classifiers. The objective of this research is to handle the difficulty of multi-class imbalanced imag...
Published in: | International Journal on Informatics Visualization |
---|---|
Main Author: | Gao X.; Jamil N.; Ramli M.I.; Ariffin S.M.Z.S.Z. |
Format: | Article |
Language: | English |
Published: |
Politeknik Negeri Padang
2024
|
Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189610439&doi=10.62527%2fjoiv.8.1.2194&partnerID=40&md5=fde1ce93c2d09526862bd2a73f948536 |
Similar Items
-
Comparison of ensemble hybrid sampling with bagging and boosting machine learning approach for imbalanced data
by: 2-s2.0-85142097924
Published: (2023) -
CL-SR: Boosting Imbalanced Image Classification with Contrastive Learning and Synthetic Minority Oversampling Technique Based on Rough Set Theory Integration
by: Gao X.; Jamil N.; Ramli M.I.
Published: (2024) -
Improving transformer failure classification on imbalanced DGA data using data-level techniques and machine learning
by: Azmi, et al.
Published: (2025) -
Improving clustering-based and adaptive position-aware interpolation oversampling for imbalanced data classification
by: Wang Y.; Rosli M.M.; Musa N.; Wang L.
Published: (2024) -
Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks
by: 2-s2.0-85164711241
Published: (2023)