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...
相似书籍
-
Comparison of ensemble hybrid sampling with bagging and boosting machine learning approach for imbalanced data
由: 2-s2.0-85142097924
出版: (2023) -
CL-SR: Boosting Imbalanced Image Classification with Contrastive Learning and Synthetic Minority Oversampling Technique Based on Rough Set Theory Integration
由: Gao X.; Jamil N.; Ramli M.I.
出版: (2024) -
Improving transformer failure classification on imbalanced DGA data using data-level techniques and machine learning
由: Azmi, et al.
出版: (2025) -
Improving clustering-based and adaptive position-aware interpolation oversampling for imbalanced data classification
由: Wang Y.; Rosli M.M.; Musa N.; Wang L.
出版: (2024) -
Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks
由: 2-s2.0-85164711241
出版: (2023)